collection curated by joecohen https://academictorrents.com/collection/video-lectures There are 8 modules in this course: This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information. https://academictorrents.com/download/4a16e5c3b12ad16246ff773337851ddca669d0a5 Visit >>> Coursera - Programming for Designers Specialization Course details Develop a foundation in Computational Design. Explore Creative Coding with Python What you ll learn - Learn the fundamentals of Python programming, including essential coding techniques - Engage in computational design thinking to approach design problems with a mindset that leverages computational strategy and problem-solving - Understand how to develop custom algorithms that can generate a range of design solutions against complex requirements, constraints, and objectives - Demonstrate the application of computational methods in design-related disciplines using a variety of computational tools Specialization - 3 course series In Programming for Designers, you will explore Python programming within a creative context, equipping you with essential computational design skills. Beginning with fundamental programming principles, you will move on to more intricate data structures, leading to the development of practical creative coding projects. Learn how to use the Processing platform, a program that allows designers to create visual, interactive media to meet their project needs. Develop the skills to move from simple to intricate designs, ranging from illustrative shapes and images to animations. Cover procedural best practices for design applications and intelligence navigation, and build a rich understanding of how advanced data structures can be used to create digital environments. This course series is tailored for individuals within architecture, graphic design, industrial design, game design and the visual arts interested in integrating programming with graphic creativity. As each course in the series is structured to build on previous course knowledge, Programming for Designers allows you to practice your skills within Python, allowing you to bring your design concepts to life with precision and efficiency. Applied Learning Project Participants will create graphic applications in Python through the Processing environment. Access to a comprehensive series of design examples tailored for the course is provided, along with instructions to build each from the ground up. This approach covers essential principles and leads to the development of personalized creative applications. Skills you’ll gain - Object Oriented Programming Language - Programming graphics - Design - Computational Design - Data Structures - Python Programming - Object Oriented Programming (OOP) - Processing (Programming environment) - Computational thinking Author: Jose Sanchez, Offered by University of Michigan General Details: Duration: 31h 18m 21s Updated: 04/2025 Language: English Subtitle: .SRT Included Source: MP4 | Video: AVC, 1280x720p | Audio: AAC, 44.100 KHz, 2 Ch | Course material included https://academictorrents.com/download/f58a74aad31ae9ac4c14f3e30414d93fe8c1ce8d Go is an open source programming language created by Google. As one of the fastest growing languages in terms of popularity, its a great time to pick up the basics of Go! This course is designed to get you up and running as fast as possible with Go. We ll quickly cover the basics, then dive into some of the more advanced features of the language. Don t be tricked by other courses that only teach you for-loops and if-statements! This is the only course on Udemy that will teach you how to use the full power of Go s concurrency model and interface type systems. Go is designed to be easy to pick up, but tough to master. Through multiple projects, quizzes, and assignments, you ll quickly start to master the language s quirks and oddities. Go is like any other language - you have to write code to learn it! This course will give you ample opportunities to strike out on your own and start working on your own programs. In this course you will: Understand the basic syntax and control structures of the language Apply Go s concurrency model to build massively parallel systems Grasp the purpose of types, which is especially important if you re coming from a dynamically typed language like Javascript or Ruby Organize code through the use of packages Use the Go runtime to build and compile projects Get insight into critical design decisions in the language Gain a sense of when to use basic language features Go is one of the fastest-growing programming languages released in the last ten years. Get job-ready with Go today by enrolling now! https://academictorrents.com/download/773b405dc903c094b7fa2ce450adad066b277490 Visit >>> Coursera - Psychedelic Science And Medicine 2025 Course details Explore the science and therapeutic potential of psychedelics in this course led by experts from the Johns Hopkins Center for Psychedelic and Consciousness Research. You ll learn about the history of psychedelic use, the neuroscience underlying their effects, and the latest clinical trials evaluating their therapeutic potential. Gain insights into their risks and benefits, ethical considerations like informed consent, and ongoing challenges in the field. This course equips learners to critically assess scientific findings, moving beyond hype to understand the evidence. Whether you re interested in the neurobiological mechanisms of psychedelics, their role in mental health treatment, or broader societal impacts, this course provides a comprehensive and evidence-based foundation. Perfect for students, professionals, or anyone curious about this emerging area of science, the course offers a unique opportunity to engage with cutting-edge research and practical implications for medicine and beyond. There are 4 modules in this course Offered by - Johns Hopkins University General Details: Duration: 5h 51m Updated: 04/2025 Language: English Source: MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch | 45 Lectures | 20 PDF, 4 HTML https://academictorrents.com/download/1322db11e9b442caac35a9cdfffa271d98819b39 Official Course URL: udemy.com/course/adobe-premiere-pro-video-editing/ Course Overview: This comprehensive course is designed to transform you into a confident video editor with Adobe Premiere Pro CC. From basic editing techniques to advanced effects, motion graphics, and audio editing, you ll master all aspects of video production through hands-on practice with included project files. What You ll Learn: - Editing Basics: Start a project, add transitions, and edit audio and video seamlessly. - Color Grading: Correct and grade videos to enhance their aesthetic appeal. - Motion Graphics: Create dynamic titles, motion effects, and overlays. - Green Screen & Effects: Edit chroma key footage and apply visual effects. Course Benefits: - Beginner-Friendly: Ideal for creators with no prior experience in Premiere Pro. - Advanced Techniques: Learn efficiency tips and advanced editing workflows. - Hands-On Learning: Practice with real-world projects using supplied video and audio clips. - Career Growth: Build confidence and skills to start editing professionally. Total Hours of Course: 25 hours 43 minutes Course Size: 6.15 GB Subtitles: English, Persian Who is this course for? This course is perfect for beginners, transitioning editors, or anyone aiming to master Adobe Premiere Pro for professional or personal video projects. https://academictorrents.com/download/1e6b74db6defaac37ef293d9c7ad667ea2ab87d9 Official Course URL: udemy.com/course/after-effects-cc-bootcamp/ Course Overview: Master Adobe After Effects CC with this comprehensive bootcamp, covering Motion Graphics, Visual Effects, and VFX Compositing. Engage in over 55 real-world projects, from beginner to advanced levels, to unleash your creativity and build professional-grade animations and video effects. What You ll Learn: - Motion Graphics Mastery: Create stunning designs and animations using advanced techniques. - Visual Effects Skills: Grasp motion tracking, chroma keying, rotoscoping, and camera tracking. - 3D Animation: Work with 3D cameras, lights, and shadows for immersive motion graphics. - Project-Based Learning: Complete 55+ practical projects, from basics to complex effects. 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Total Hours of Course: 9 hours 15 minutes Course Size: 1.11 GB Subtitles: English, Persian Who is this course for? This course is ideal for beginners, non-IT professionals, and anyone interested in starting a career in software testing. https://academictorrents.com/download/cbec20f4ade504701c1bd308913cc5df10eb4d6b Official Course URL: udemy.com/course/master-of-essential-c-programming-beginner-to-advanced/ Course Overview: Step into the world of programming with this beginner-friendly course on C++ programming. Designed for complete novices and intermediate learners, this course provides a hands-on approach to mastering C++ fundamentals, core programming concepts, and object-oriented principles. Through practical exercises and real-world examples, you ll gain confidence in writing structured and optimized C++ programs. What You ll Learn: - Core Concepts: Grasp variables, data types, operators, and control flow. - Programming Basics: Learn functions, modules, arrays, pointers, and strings. - Object-Oriented Principles: Dive into classes, objects, inheritance, and polymorphism. - Real-World Applications: Build practical C++ programs through engaging projects. Course Benefits: - Beginner-Friendly: No prior programming knowledge required. - Hands-On Learning: Apply concepts through exercises and projects. - Career Preparation: Build a strong foundation for software development roles. - Accessible Teaching: Easy-to-follow content for all levels. Total Hours of Course: 5 hours 44 minutes Course Size: 629.9 MB Subtitles: English, Persian Who is this course for? This course is ideal for high school or college students, aspiring software developers, and anyone looking to start their journey in C++ programming. https://academictorrents.com/download/8bd786e2ee84c05cd650e56f7f6232e73d66756a Official Course URL: udemy.com/course/sql-for-financial-data-analysis/ Course Overview: Unlock the power of SQL for financial data analysis and reporting. This course is tailored for non-tech professionals who want to streamline their analytics and reporting capabilities. Learn to extract and process financial data, prepare detailed reports like Profit & Loss Statements and Balance Sheets, and calculate critical financial ratios through practical exercises. What You ll Learn: - SQL Basics: Master database querying techniques for financial data. - Report Preparation: Create Profit & Loss Statements, Balance Sheets, and Cash Flow Statements. - Key Analytics: Calculate and interpret profitability, efficiency, and liquidity ratios. - Database Skills: Gain hands-on experience without prior technical expertise. Course Benefits: - Practical Applications: Apply SQL to real-world financial scenarios. - Independent Reporting: Reduce reliance on system-generated reports. - Career Advancement: Enhance your data analysis and reporting skills. - Expert Guidance: Learn from an experienced Chartered Accountant. Total Hours of Course: 5 hours 29 minutes Course Size: 701 MB Subtitles: English, Persian Who is this course for? This course is ideal for Financial Analysts, Business Analysts, Accountants, Bookkeepers, and Finance Professionals looking to enhance their data analysis capabilities using SQL. https://academictorrents.com/download/161282939abe2462e37cd8a59664043716a1a529 Official Course URL: udemy.com/course/learn-git-essentials-2024/ Course Overview: Unlock the potential of Git with this beginner-to-advanced course designed for developers. "Learn Git Essentials [2024]" provides an engaging guide to mastering version control for efficient coding and collaboration. With 39 lessons, it offers hands-on experience with Git commands, workflows, and advanced techniques to streamline development. What You ll Learn: - Git Basics: Set up Git, understand core concepts, and practice essential commands. - Branching & Merging: Manage branches and resolve merge conflicts like a pro. - Remote Repos: Work with GitHub, GitLab, and Bitbucket for collaborative projects. - Advanced Tools: Learn rebasing, stashing, Git hooks, and best practices for efficiency. Course Benefits: - Beginner-Friendly: Start from scratch with no prior Git knowledge required. - Hands-On Demos: Practice real-world workflows with guided exercises. - Collaborative Skills: Master Git for teamwork and version control. - Comprehensive Content: Gain advanced Git techniques to optimize your projects. Total Hours of Course: 4 hours 30 minutes Course Size: 606.8 MB Subtitles: English, Persian Who is this course for? This course is perfect for aspiring developers and software engineers looking to master Git for version control and collaboration. https://academictorrents.com/download/7fe8b6d871e0a43fd4312c6ef0fe7bf2b485f766 Official Course URL: udemy.com/course/the-complete-hands-on-introduction-to-airbyte/ Course Overview: Master Airbyte, the powerful open-source data integration tool, in this beginner-friendly course by Marc Lamberti. Learn to consolidate data from various sources into your data warehouses or databases seamlessly. Discover how to use Airbyte alongside tools like Apache Airflow, dbt, and Snowflake to build robust pipelines. With 56 practical lessons, this course equips you to run efficient data synchronizations, set up notifications, and manage data pipelines effectively. What You ll Learn: - Airbyte Essentials: Understand its architecture, features, and role in data integration. - Hands-On Setup: Install and configure Airbyte locally with Docker and Kubernetes. - Data Integration: Connect Airbyte to multiple sources and destinations for seamless syncs. - Pipeline Building: Create end-to-end pipelines with dbt, Airflow, Postgres, Snowflake, and more. - Best Practices: Optimize workflows, set up monitoring, and manage notifications efficiently. Course Benefits: - Beginner-Friendly Approach: Perfect for those starting in data integration and pipeline building. - Extensive Toolset: Learn how to use Airbyte with complementary tools like dbt and Snowflake. - Practical Learning: Apply knowledge through hands-on exercises and a real-world project. - Lifetime Access: Revisit content anytime with lifetime course access. Total Hours of Course: 3 hours 17 minutes Course Size: 480.4 MB Subtitles: English, Persian Who is this course for? This course is designed for Data Engineers, Analytics Engineers, and Data Architects looking to integrate Airbyte into their workflows efficiently. https://academictorrents.com/download/2171960ee66c07fd5f251eac81268ca0a40723b5 https://academictorrents.com/download/fb014a1ffea0158f6104c3f51cd1e7724596bcc9 https://academictorrents.com/download/fe4cc754cddc2a14d3f25ce95e37f02fb051b8a4 Official Course URL: udemy.com/course/laravel-11-from-basics-to-advance/ Course Overview: Dive into Laravel 11 - From Basics to Advance (2024), a comprehensive course designed to take you from foundational concepts to advanced techniques in Laravel 11. Whether you re a newcomer to web development or looking to enhance your skills, this course offers a structured path to mastering Laravel, enabling you to build professional-grade applications with confidence. What You ll Learn: - Routing and Controllers: Understand the flow of requests and responses in Laravel applications. - Blade Templates and Views: Create dynamic and reusable user interfaces efficiently. - Database Management: Utilize Models, Migrations, Seeders, and the Eloquent ORM for effective data handling. - Form Handling and Validation: Implement robust forms with built-in validation mechanisms. - File Storage and Management: Manage file uploads and storage seamlessly. - Middleware and HTTP Responses: Control request processing and customize responses. - Authentication and Authorization: Secure your applications with user authentication and access control. - Mail and Notifications: Integrate email functionalities for user communication. - Blade Components and Session Management: Enhance modularity and maintain user sessions. - Advanced Topics: Explore Queues, Background Processing, Observers, Event Listeners, Broadcasting, Service Container, and API development. - Hands-on Projects: Build real-world applications like an E-commerce Cart, Real-Time Messenger, and a Google Keep Clone to solidify your learning. Course Benefits: - Expert Instruction: Learn from seasoned developers with extensive experience in Laravel. - Practical Approach: Engage in hands-on projects that mirror real-world scenarios. - Comprehensive Curriculum: Gain a deep understanding of both basic and advanced Laravel concepts. - Flexible Learning: Access course materials anytime with lifetime access. - Community Support: Join a community of learners and receive assistance through Q&A forums. Total Hours of Course: 51 hours 44 minutes Course Size: 4.58 GB Subtitles: English, Persian Who is this course for? This course is ideal for beginners in web development, aspiring Laravel developers, junior developers seeking to enhance their skills, PHP developers transitioning to Laravel, freelancers, entrepreneurs, students, educators, and professionals aiming for career growth in web development. https://academictorrents.com/download/da3b771fb498e367bc146806c4f192d590f24352 This class uses revolutionary programmable interactivity to combine material from three fields creating an engaging, efficient learning solution to prepare students to be sophisticated and intuitive thinkers, programmers, and solution providers for the modern interconnected online world. Upon completion, students are well trained to be scientific “trilinguals”, seeing and experimenting with mathematics interactively as math is meant to be seen, and ready to participate and contribute to open source development of large projects and ecosystems. More info: https://academictorrents.com/download/d72ad3cf23be9be6df34240d3ea24b3071003d9e Psychology and Economics (aka Behavioral Economics) is a growing subfield of economics that incorporates insights from psychology and other social sciences into economics. This course covers recent advances in behavioral economics by reviewing some of the assumptions made in mainstream economic models, and by discussing how human behavior systematically departs from these assumptions. Applications will cover a wide range of fields, including labor and public economics, industrial organization, health economics, finance, and development economics. https://academictorrents.com/download/c4f6dbdff92ef7455976f38568969684e085aa1d This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses. https://academictorrents.com/download/2437f684caa1a06b0c3aad7dc184e3f89f897776 @article{, title= {NORD Osmosis Videos 2023}, keywords= {}, author= {}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/c5632854d1f5aa3740003cd4cf5b8b90d4851584 @article{, title= {React & TypeScript Chrome Extension Development [2023]}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {React, Web Development, typescript, chrome, extension}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/bd13646530182a285ddca32f1ad61d0e04a63b2f These are the Open Osmosis videos which have been released under the CC-BY-SA 4.0 license; the videos were created in a collaboration with WikiProject Medicine and are available on Wikimedia Commons. https://academictorrents.com/download/4f491f0e13d763408ab6ff87cf7a8ddc48228806 ## About Learn PyTorch from scratch! This PyTorch course is your step-by-step guide to developing your own deep learning models using PyTorch. You’ll learn Deep Learning with PyTorch by building a massive 3-part real-world milestone project. By the end, you’ll have the skills and portfolio to get hired as a Deep Learning Engineer. Learn PyTorch. Become a Deep Learning Engineer. Get Hired. ## Course Overview We can guarantee (with, like, 99.57% confidence) that this is the most comprehensive, modern, and up-to-date course you will find to learn PyTorch and the cutting-edge field of Deep Learning. Daniel takes you step-by-step from an absolute beginner to becoming a master of Deep Learning with PyTorch. ## What You’ll Learn – Everything from getting started with using PyTorch to building your own real-world models – Why PyTorch is a fantastic way to start working in machine learning – Understand how to integrate Deep Learning into tools and applications – Create and utilize machine learning algorithms just like you would write a Python program – Build and deploy your own custom trained PyTorch neural network accessible to the public – How to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applications – Master deep learning and become a top candidate for recruiters seeking Deep Learning Engineers – To expand your Machine Learning and Deep Learning skills and toolkit – The skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / year ## What will this PyTorch course be like? This PyTorch course is very hands-on and project based. You won’t just be staring at your screen. We’ll leave that for other PyTorch tutorials and courses. In this course you’ll actually be: – Running experiments – Completing exercises to test your skills – Building real-world deep learning models and projects to mimic real life scenarios By the end of it all, you’ll have the skillset needed to identify and develop modern deep learning solutions that Big Tech companies encounter. ⚠ Fair warning: this course is very comprehensive. But don’t be intimidated, Daniel will teach you everything from scratch and step-by-step! ## Here’s what you’ll learn in this PyTorch course: 1. PyTorch Fundamentals — We start with the barebone fundamentals, so even if you’re a beginner you’ll get up to speed. In machine learning, data gets represented as a tensor (a collection of numbers). Learning how to craft tensors with PyTorch is paramount to building machine learning algorithms. In PyTorch Fundamentals we cover the PyTorch tensor datatype in-depth. 2. PyTorch Workflow — Okay, you’ve got the fundamentals down, and you’ve made some tensors to represent data, but what now? With PyTorch Workflow you’ll learn the steps to go from data -> tensors -> trained neural network model. You’ll see and use these steps wherever you encounter PyTorch code as well as for the rest of the course. 3. PyTorch Neural Network Classification — Classification is one of the most common machine learning problems. – Is something one thing or another? – Is an email spam or not spam? – Is credit card transaction fraud or not fraud? With PyTorch Neural Network Classification you’ll learn how to code a neural network classification model using PyTorch so that you can classify things and answer these questions. 4. PyTorch Computer Vision — Neural networks have changed the game of computer vision forever. And now PyTorch drives many of the latest advancements in computer vision algorithms. For example, Tesla use PyTorch to build the computer vision algorithms for their self-driving software. With PyTorch Computer Vision you’ll build a PyTorch neural network capable of seeing patterns in images of and classifying them into different categories. 5. PyTorch Custom Datasets — The magic of machine learning is building algorithms to find patterns in your own custom data. There are plenty of existing datasets out there, but how do you load your own custom dataset into PyTorch? This is exactly what you’ll learn with the PyTorch Custom Datasets section of this course. You’ll learn how to load an image dataset for FoodVision Mini: a PyTorch computer vision model capable of classifying images of pizza, steak and sushi (am I making you hungry to learn yet?!). We’ll be building upon FoodVision Mini for the rest of the course. 6. PyTorch Going Modular — The whole point of PyTorch is to be able to write Pythonic machine learning code. There are two main tools for writing machine learning code with Python: – A Jupyter/Google Colab notebook (great for experimenting) – Python scripts (great for reproducibility and modularity) In the PyTorch Going Modular section of this course, you’ll learn how to take your most useful Jupyter/Google Colab Notebook code and turn it reusable Python scripts. This is often how you’ll find PyTorch code shared in the wild. 7. PyTorch Transfer Learning — What if you could take what one model has learned and leverage it for your own problems? That’s what PyTorch Transfer Learning covers. You’ll learn about the power of transfer learning and how it enables you to take a machine learning model trained on millions of images, modify it slightly, and enhance the performance of FoodVision Mini, saving you time and resources. 8. PyTorch Experiment Tracking — Now we’re going to start cooking with heat by starting Part 1 of our Milestone Project of the course! At this point you’ll have built plenty of PyTorch models. But how do you keep track of which model performs the best? That’s where PyTorch Experiment Tracking comes in. Following the machine learning practitioner’s motto of experiment, experiment, experiment! you’ll setup a system to keep track of various FoodVision Mini experiment results and then compare them to find the best. 9. PyTorch Paper Replicating — The field of machine learning advances quickly. New research papers get published every day. Being able to read and understand these papers takes time and practice. So that’s what PyTorch Paper Replicating covers. You’ll learn how to go through a machine learning research paper and replicate it with PyTorch code. At this point you’ll also undertake Part 2 of our Milestone Project, where you’ll replicate the groundbreaking Vision Transformer architecture! 10. PyTorch Model Deployment — By this stage your FoodVision model will be performing quite well. But up until now, you’ve been the only one with access to it. How do you get your PyTorch models in the hands of others? That’s what PyTorch Model Deployment covers. In Part 3 of your Milestone Project, you’ll learn how to take the best performing FoodVision Mini model and deploy it to the web so other people can access it and try it out with their own food images. ## Meet your instructor Your PyTorch instructor (Daniel) isn’t just a machine learning engineer with years of real-world professional experience. He has been in your shoes. He makes learning fun. He makes complex topics feel simple. He will motivate you. He will push you. And he will go above and beyond to help you succeed. Hi, I’m Daniel Bourke! Daniel, a self-taught Machine Learning Engineer, has worked at one of Australia’s fastest-growing artificial intelligence agencies, Max Kelsen, and is now using his expertise to teach thousands of students data science and machine learning. ## General Info: Author(s): Daniel Bourke Language: English Updated: 2/2023 Videos Duration: 49h 2m 32s Course Source: https://academictorrents.com/download/b51e3cc0cc631bde4b1fe866a6d171046873b181 @article{, title= {Udemy - MongoDB - The Complete Developer's Guide}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/67511d8777c824dcde4ceba01cfc9c4931c4df6a @article{, title= {Udemy - Angular & NodeJS - The MEAN Stack Guide}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/0aa8701e7ac69f17b3528876fbfb50d4ae0aa9a4 @article{, title= {Udemy - The Complete Flutter Development Bootcamp with Dart}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/99433c1797b219a1ec5dcec21fa4213881e5b194 @article{, title= {Udemy - Artificial Intelligence A-Z Learn How To Build An AI}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/750ab85e01a0d443bd0d19e49b250f8896e1e791 @article{, title= {Udemy - Deep Learning and Computer Vision A-Z OpenCV, SSD & GANs}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/d131e045c7fec5256025d72035a00e9fc69f24e7 @article{, title= {Udemy - Docker and Kubernetes The Complete Guide}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/1f8c426ea3909c9b5b083f06bebcfee24fea03ae @article{, title= {Udemy - Learn Ethical Hacking From Scratch}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/dd0942e4a1782c0a662e9c16682ab977da156670 @article{, title= {Udemy - NodeJS - The Complete Guide (incl. MVC, REST APIs, GraphQL)}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/d10602c009ef3b4794994976476399865c76956d @article{, title= {Udemy - The Complete Android N Developer Course}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/d9b124bec55927b282f19dd143b367f0f3f79be1 @article{, title= {Udemy - Machine Learning A-Z Become Kaggle Master}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/9e378efb6e2f67de46c6c3660d9675be50bfc21f @article{, title= {Coursera - Python 3 Programming Specialization}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/83918ea4bb488cefd3d8b8b8762597d32aebb4fa @article{, title= {Coursera - Data Science Fundamentals with Python and SQL}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/bdc0bb1499b1992a5488b4bbcfc9288c30793c08 @article{, title= {Coursera - Applied Data Science with Python}, journal= {}, author= {}, year= {}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/c9ef88cfe0137f6a4292823f0765a5d4b93ff313 Video-Lezioni per il corso di Informatica, per il primo anno dei corsi di Ingegneria tenutosi al Politecnico di Torino nell Anno Accademico 2020/2021. Docenti del corso: Fulvio Corno, Juan Pablo Saenz Moreno, Luisa Fernanda Barrera Leon Informazioni sul corso: - pagina ufficiale del corso: Queste video-lezioni sono disponibili anche come playlist su YouTube: https://academictorrents.com/download/2eab1a9c70d636b14c4f2118f3818875e75660a8 This content hosted at the Internet Archive at . https://academictorrents.com/download/8835f0ca640ca9405f32c7d00c140cc16fcfa078 This tutorial will be styled as a graduate lecture about medical imaging with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks. These methods will be covered in terms of architecture and objective function design. Also, a discussion about incorrect feature attribution and approaches to mitigate the issue. Prerequisites: basic knowledge of computer vision (CNNs) and machine learning (regression, gradient descent). Presented by: Joseph Paul Cohen PhD Postdoctoral Fellow Mila, University of Montreal https://academictorrents.com/download/e0974c84449826e34d8cc96c943cba2af18ab514 For centuries we have collectively marveled at plant diversity and form—from Charles Darwin’s early fascination with stems and flowers to Seymour Krelborn’s distorted doting in Little Shop of Horrors. This course intends to present an intriguing and scientifically valid look at how plants themselves experience the world—from the colors they see to the sensations they feel. Highlighting the latest research in genetics and more, we will delve into the inner lives of plants and draw parallels with the human senses to reveal that we have much more in common with sunflowers and oak trees than we may realize. We’ll learn how plants know up from down, how they know when a neighbor has been infested by a group of hungry beetles, and whether they appreciate the music you’ve been playing for them or if they’re just deaf to the sounds around them. We’ll explore definitions of memory and consciousness as they relate to plants in asking whether we can say that plants might even be aware of their surroundings. This highly interdisciplinary course meshes historical studies with cutting edge modern research and will be relevant to all humans who seek their place in nature. This class has three main goals: 1. To introduce you to basic plant biology by exploring plant senses (sight, smell, hearing, touch, taste, balance). 2. To introduce you to biological research and the scientific method. 3. To get the student to question life in general and what defines us as humans. Once you ve taken this course, if you are interested in a more in-depth study of plants, check out my follow-up course, Fundamentals of Plant Biology (). In order to receive academic credit for this course you must successfully pass the academic exam on campus. For information on how to register for the academic exam – Additionally, you can apply to certain degrees using the grades you received on the courses. Read more on this here – Teachers interested in teaching this course in their class rooms are invited to explore our Academic High school program here – https://academictorrents.com/download/81ff5fc1df7c1fb9300e9712368dfc479427004d @article{, title= {[Coursera] Natural Language Processing (Dan Jurafsky and Chris Manning)}, journal= {}, author= {Dan Jurafsky and Chris Manning}, year= {2012}, url= {}, abstract= {}, keywords= {}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/9ad3c282ff6c4137ed8b073d884ea3d72c2e4cd1 Video Lectures of the course "Web Applications I" taken at Politecnico di Torino (Italy), in year 2019/2020. https://academictorrents.com/download/ffdcaa1b2bca554612545c0394318d60e6b3796b Video-Lezioni per il corso di Tecniche di Programmazione, tenutosi al Politecnico di Torino nell Anno Accademico 2019/2020. Docenti del corso: Fulvio Corno, Alberto Monge Roffarello, Tatiana Tommasi Informazioni sul corso: - pagina ufficiale del corso: - materiale didattico: - esercizi e laboratori: - temi d esame: Queste video-lezioni sono disponibili anche come playlist su YouTube: https://academictorrents.com/download/be289c8dceaa7ef16eb2969431c7e8330285c7f4 When one practices medicine in their home country there are standards of care and guidelines often established by societies and organizations. Many look to the World Health Organization when practicing abroad and in limited resource countries. Many WHO guidelines can be found online such as the WHO Model Prescribing Information: Drugs used in Bacterial Infections (). It is important and can create for a better dynamic when interacting with local providers to be aware of any country specific guidelines that may be created and published for a particular country. These may be created by societies or organizations in a particular country or even by the country’s Health Ministry (e.g., Uganda Clinical Guidelines content/uganda-clinical-guidelines-2016 ). Two major features of practicing in limited resource settings are usually a limited number of available diagnostic tests and a limited medication formulary. Included in this handbook are examples of basic tests that might be available as well as a few examples of basic formularies. In many countries there are ‘required’ medications and these may be stocked despite no obvious local need. Selecting medications and management algorithms often involve complex decisions based on finite and often limited pharmacy budgets and the local availability and cost of medications. Deciding to stock, prescribe and dispense the latest name brand antihypertensive for an elderly individual with a slightly elevated blood pressure may result in not having malaria medications for a critically ill child. The following executive summaries are a starting point for the understanding, diagnosis and treatment of common presentations one is likely to encounter in low resource setting as well as specific diseases. The final decisions regarding how these presentations and diseases are approached and managed should however be based on the judgement of a medical profession familiar with the local epidemiology, customs, and standards of care in a particular region. Several aspects of this guide will serve only as the foundation of further judgement on the part of the clinician. As far as dosing recommendations, if the dosing is 3x/day a clinician will need to communicate with the patient or caregiver regarding if this is 3 times per day with meals, every 8 hours or is maximum per day. Not only does this aspect of care require judgement but it also is greatly improved by understanding the cultural context. In certain cultures, 3 meals are customary while in others a morning and evening meal are the norm with a late morning break for tea. Another area where cultural sensitivity is critical are issues surrounding family planning. If one administers an intramuscular contraceptive in a women’s shoulder below the area covered by the shirt sleeve and then affixes a band aid over the site the patient’s privacy may be compromised with negative consequences. Recommending Co-trimoxazole (trimethoprim-sulfa) to a patient with a urinary tract infection in Sub-Saharan Africa might seem to make perfect sense while a patient might be confused and upset as the widespread use of this as a prophylactic medication in the HIV-infected population has led to this medication being viewed as an ‘HIV medication’. Visit https://academictorrents.com/download/08078e0894099530913e9335bdbc804f42f6e872 Lectures of Ambient Intelligence at Politecnico di Torino, in 2019. Topics: * Introduction to Ambient Intelligence: definitions and available approaches for smart homes, smart buildings, etc. Overview of application areas (home, building, city, traffic, etc.) and types of applications (monitoring, comfort, anomaly detection, ambient assisted living, control and automation, etc.) * Requirements and design methodology for AmI. Design, analysis and specification of requirements and functionalities related to user interacting with AmI settings. * Practical programming of AmI systems: the Python language, the Raspberry Pi computer, Web protocols and languages (e.g., HTTP and REST), web-based APIs, and collaboration tools (git, GitHub). https://academictorrents.com/download/9bbe28468af204ccefe75662cd184ce0abed0ad4 Video Lezioni corso di Tecniche di Programmazione, Politecnico di Torino, anno 2019 (in Italian) Video-Lezioni per il corso di Tecniche di Programmazione, tenutosi al Politecnico di Torino nell Anno Accademico 2018/2019. Docenti del corso: Fulvio Corno, Andrea Marcelli, Alberto Monge Roffarello Informazioni sul corso: - pagina ufficiale del corso: - materiale didattico: - esercizi e laboratori: - temi d esame: Queste video-lezioni sono disponibili anche come playlist su YouTube: https://academictorrents.com/download/829147695b59dfa6a63676493525c350e0968ae8 “Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market. In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study. https://academictorrents.com/download/9bd4c5afe4f80a0b0e519923ff1eb5c76e5c07a9 Video Lectures of the short course on Semantic Web taken at Politecnico di Torino (Italy), in January 2019. https://academictorrents.com/download/41c49b7b3ef5d6e2f02ba9b09525dd8d377b72ca The Analytics Edge Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. About this course Skip Course Description In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications. The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a “quick question” to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we’ll use in the course. See the Software FAQ below for more info). At the end of the class there will be a final exam, which will be similar to the homework assignments. What you ll learn An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization How to implement all of these methods in R An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software Prerequisites Basic mathematical knowledge (at a high school level). You should be familiar with concepts like mean, standard deviation, and scatterplots. Mathematical maturity and prior experience with programming will decrease the estimated effort required for the class, but are not necessary to succeed. https://academictorrents.com/download/1d4ec1fc1aca061e71cc504f93a7f9fca9ef7311 Video-lezioni Sistemi Informativi Aziendali https://academictorrents.com/download/a5f1f19c2f6872cbc83c460e372f0ae7e15bf770 https://academictorrents.com/download/35b6b8bf0c2931ba7ecd8a1a8e65fa32f3e7473f Video Lecture of Course Data Mining & Machine Learning by Prof Pedro Domingos, University of Washington USA. https://academictorrents.com/download/0db676a6aaff8c33f9749d5f9c0fa22bf336bc76 Stanford course on Convolutional Neural Networks for Visual Recognition # Course Description Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge. https://academictorrents.com/download/ed8a16ebb346e14119a03371665306609e485f13 (Including videos, presentation files and subtitles for the videos [ENG only]) ## About the Course The impact of technology and networks on our lives, culture, and society continues to increase. The very fact that you can take this course from anywhere in the world requires a technological infrastructure that was designed, engineered, and built over the past sixty years. To function in an information-centric world, we need to understand the workings of network technology. This course will open up the Internet and show you how it was created, who created it and how it works. Along the way we will meet many of the innovators who developed the Internet and Web technologies that we use today. ## What You Will Learn After this course you will not take the Internet and Web for granted. You will be better informed about important technological issues currently facing society. You will realize that the Internet and Web are spaces for innovation and you will get a better understanding of how you might fit into that innovation. If you get excited about the material in this course, it is a great lead-in to taking a course in Web design, Web development, programming, or even network administration. At a minimum, you will be a much wiser network citizen. ## Course Syllabus * Week 1: Introduction to the Course and The Dawn of Electronic Computing (1940-1960) * Week 2: The First Internet (1960-1990) * Week 3: The World Wide Web (1990-1995) * Week 4: Commercialization and Growth (1995-2000) * Week 5: Internets and Packets * Week 6: Transports and Security * Week 7: Networked Applications * Week 8: Security - Protecting Information * Week 9: Security - Establishing Identity * Final Exam ## Recommended Background This course has no prerequisites and there will be no programming. Literally anyone can and everyone should take this course. https://academictorrents.com/download/d666bf4b83066bcf7401e9c155fcd1b1c01cfb11 "Demanding, but definitely doable. Social, but educational. A focused topic, but broadly applicable skills. CS50 is the quintessential Harvard (and Yale!) course. Hello, world! This is CS50 (aka CS50x through edX), Harvard University s introduction to the intellectual enterprises of computer science and the art of programming. Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Designed for majors and non-majors alike, with or without prior programming experience. https://academictorrents.com/download/52da574b6412862e199abeaea63e51bf8cea2140 Volume I - mainly mechanics, radiation, and heat Volume II - mainly electromagnetism and matter Volume III - quantum mechanics https://academictorrents.com/download/c5af268ec55cf2d3b439e7311ad43101ba8322eb Biostatistical concepts and modeling relevant to the design and analysis of multifactor population-based cohort and case-control studies, including matching. Measures of association, causal inference, confounding interaction. Introduction to binary regression, including logistic regression. https://academictorrents.com/download/8469a366bf4b71ff62d9e2327537771bdc145dfa This introductory course is designed to explore fundamental legal and policy issues in environmental law. Through examination of environmental common law and key federal environmental statutes, including the National Environmental Policy Act, Clean Air Act, and Clean Water Act, it exposes students to the major challenges to environmental law and the principal approaches to meeting those challenges, including litigation, command and control regulation, technology forcing, market incentives, and information disclosure requirements. With the addition of cross-cutting topics such as risk assessment and environmental federalism, it also gives students a grounding in how choices about regulatory standards and levels of regulatory authority are made. https://academictorrents.com/download/c878ea12eff8c99cc6b1983a2d7724a18bb1a94d This course introduces students to a broad range of issues, concepts, and approaches integral to the study of peace and conflict. Subject areas include the war system and war prevention, conflict resolution and nonviolence, human rights and social justice, development and environmental sustainability. Required of all Peace and Conflict Studies majors. https://academictorrents.com/download/0ec86c151be43be4857e2da370fb5508ff418146 Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression. https://academictorrents.com/download/56b38a7013673c92cb951eb79bcd3a26e8158095 Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression. https://academictorrents.com/download/4d505fe0b3cbcbeff32bfd7b75a783f900dc8c6d This course is designed as a comprehensive overview of intermediate macroeconomic theory focusing on economic growth and international economics. It covers a number of topics including history of economic growth, industrial revolution, post-industrial revolution divergence, flexible-price and sticky-price macroeconomics, and macroeconomic policy. Course is structured for majors in International and Area Studies and other non-economic social science majors. https://academictorrents.com/download/97e704bba2ad3fb2dc5d932f4ed693fcb2f85b30 Math 53 - Section 1 - Multivariable Calculus Instructor: Edward Frenkel Lectures: TT 3:30-5:00pm, Room 155 Dwinelle Course Control Number: 54296 Office: 819 Evans Office Hours: TBA Prerequisites: Math 1A, 1B. Required Text: Stewart, Multivariable Calculus, (custom edition). Recommended Reading: Syllabus: Course Webpage: To be linked from Grading: 25% quizzes and HW, 20% each midterm, 35% final Homework: Homework for the entire course will be assigned at the beginning of the semester, and weekly homework will be due at the beginning of each week. Comments: Students have to make sure that they have no scheduling conflicts with the final exam. Missing final exam means automatic Fail grade for the entire course. https://academictorrents.com/download/d90733721eb2a2ba839434decce91ce4803cbf1e Political issues facing the state of California, the United States, or the international community. https://academictorrents.com/download/e75a329db4adabdc45502c401a1c4b69712cbb98 Stoichiometry of chemical reactions, quantum mechanical description of atoms, the elements and periodic table, chemical bonding, real and ideal gases, thermochemistry, introduction to thermodynamics and equilibrium, acid-base and solubility equilibria, introduction to oxidation-reduction reactions, introduction to chemical kinetics. https://academictorrents.com/download/ebe62de0d85ba9563c566cc5b082416792bc00ca The most interesting and important topics in physics, stressing conceptual understanding rather than math, with applications to current events. Topics covered may vary and may include energy and conservation, radioactivity, nuclear physics, the Theory of Relativity, lasers, explosions, earthquakes, superconductors, and quantum physics. https://academictorrents.com/download/5140da14dd72b2a6f19a5ca08d2e2d015754909a Chemical processing and properties of solid-state materials. Crystal growth and purification. Thin film technology. Application of chemical processing to the manufacture of semiconductors and solid-state devices. https://academictorrents.com/download/f1baa15065060f1830d74111c1ef7741a73c9e98 Introduction to the principal areas, problems, and concepts of psychology https://academictorrents.com/download/687bfcdf88598c04edf98c56c3b5f838d43ec2a6 This course introduces students to key issues and findings in the field of health and environmental economics. The first half of the course focuses on the theoretical and statistical frameworks used to analyze instances of market failure in the provision of health and environmental goods. The second half focuses on policy-relevant empirical findings in the field. https://academictorrents.com/download/c633df0181d560050d3f392501c6815135cfb60e ### Course Title: Nuclear Reactions and Radiation ### Catalog Description: Energetics and kinetics of nuclear reactions and radioactive decay, fission, fusion, and reactions of energetic neutrons, properties of the fission products and the actinides; nuclear models and transition probabilities; interaction of radiation with matter. ### Course Prerequisite: Physics 7ABC Physics for scientists and engineers Prerequisite Knowledge and/or Skills: The course uses the following knowledge and skills from prerequisite and lower-division courses: - solve linear, first and second order differential equations. - understand and apply the fundamental laws of physical chemistry such as the Boltzmann distribution for particles in an ideal gas. - understand and apply the fundamentals of classical mechanics, electricity and magnetism and the elements of quantum mechanics to idealized representations of the structure of nuclei and nuclear reactions. - understand and apply the fundamental notions of probability and probability distributions. ### Course Objectives: - Provide the students with a solid understanding of the fundamentals of those aspect of low-energy nuclear physics that are most important to applications in such areas as nuclear engineering, nuclear and radiochemistry, geosciences, biotechnology, etc. ### Course Outcomes: - calculate the consequences of radioactive growth and decay and nuclear reactions. - calculate estimates of nuclear masses and energetics based on empirical data and nuclear models. - calculate estimates of the lifetimes of nuclear states that are unstable to alpha-,beta- and gamma decay and internal conversion based on the theory of simple nuclear models. - use nuclear models to predict low-energy level structure and level energies. - use nuclear models to predict the spins and parities of low-lying levels and estimate their consequences with respect to radioactive decay. - use nuclear models to understand the properties of neutron capture and the Breit-Wigner single level formula to calculate cross sections at resonance and thermal energies. - calculate the kinematics of the interaction of photons with matter and apply stopping power to determine the energy loss rate and ranges of charged particles in matter - calculate the energies of fission fragments and understand the charge and mass distributions of the fission products, and prompt neutron and gamma rays from fission ### Topics Covered: - Introduction to nuclear reactions and radioactive decay - mass and energy balances and decay modes - Nuclear and Atomic masses - empirical data and the semiempirpical mass formula - Application of the Semiempirical mass formula to determine the nuclear mass surface and the general characteristics of the energetics of alpha- and beta-decay and nuclear fission - Application of the Semiempirical mass formula to uncover empirical evidence for nuclear shell structure; the magic numbers Introduction to the facts of quantum mechanics and conserved quantities – angular momentum and parity, the Schroedinger equation and the particle in the box model - The Spherical Shell Model - particle motion , angular momentum and parity in the spherical potential well and the isotropic harmonic oscillator potentials - The Empirical Shell Model and low-lying levels of spherical and near spherical nuclei - The Electric Potential of Nuclei and Evidence for Deformed Nuclei – multipole expansion of the electric potential and empirical data on quadrupole moments - Predictions of the Quantized Rigid Rotor and Harmonic Vibrator - comparisons of the idealized models with empirical data on rotational and vibrational spectra of deformed nuclei - Alpha Decay - energetics and the decay probability in the limit of the Gamow model. Comparison of model predictions with empirical data. Alpha decay schemes - Beta Decay - beta decay, positron emission and electron capture; the Fermi theory of allowed beta decay; forbidden transitions; Fermi and Gamow-Teller decay; empirical beta decay schemes and correlations with elementary beta decay theory and spherical shell structure - Gamma Decay and Internal Conversion- multipole expansion of the radiation field and qualitative consideration of decay probabilities in the limit of the Moskowski and Weisskopf models; nuclear isomerism; internal conversion; nuclear structure and empirical data on gamma decay - Nuclear Fission - energetics and empirical data on mass distributions and shell structure, charge distribution of the fission fragments, prompt neutrons and gamma rays - Nuclear Reactions - reaction types and energetics; kinematics of two-body elastic scattering and nuclear reactions; applications to moderation of neutrons and the interaction of charged particles with matter; direct and compound nuclear reactions; resonances and physical plausibility of the form of the Breit-Wigner single level formula; the Breit-Wigner single level formula and resonances properties of neutron reactions - Introduction to the Interaction of Charged Particles with Matter; ranges of leptons and heavy charged particles in matter - Introduction to the Interaction of Photons with Matter - the Compton Effect; qualitative discussion of the effect of electron binding; pair production; macroscopic cross sections and attenuation coefficients https://academictorrents.com/download/92644e4132e893c70c3e0ad9ac1d58bef554bd14 A tour of the mysteries and inner workings of our solar system. What are planets made of? Why do they orbit the sun the way they do? How do planets form, and what are they made of? Why do some bizarre moons have oceans, volcanoes, and ice floes? What makes the Earth hospitable for life? Is the Earth a common type of planet or some cosmic quirk? This course will introduce basic physics, chemistry, and math to understand planets, moons, rings, comets, asteroids, atmospheres, and oceans. Understanding other worlds will help us save our own planet and help us understand our place in the universe. Also listed as Letters and Science C70T and Earth and Planetary Science C12. https://academictorrents.com/download/1433dd89d4366df2b534c5e3b6b267776a67e7af An introduction to research in bioengineering including specific case studies and organization of this rapidly expanding and diverse field. https://academictorrents.com/download/c4cd3183550f9cc1cfdcb69f15b076ba439ee062 @article{, title= {Public Health 150E, 001 - Spring 2015 - UC Berkeley}, keywords= {}, author= {William A. Satariano}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/057e7009bdf9e3d09b1ef56ffbd82a1d1a5de23c Catalog Description: (4 units) Discrete time signals and systems: Fourier and Z transforms, DFT, 2-dimensional versions. Digital signal processing topics: flow graphs, realizations, FFT, quantization effects, linear prediction. Digital filter design methods: windowing, frequency sampling, S-to-Z methods, frequency-transformation methods, optimization methods, 2-dimensional filter design. Prerequisites: EECS 120, or instructor permission. Course objectives: To develop skills for analyzing and synthesizing algorithms and systems that process discrete time signals, with emphasis on realization and implementation. Why should you care? Digital signal processing is one of the most important and useful tools an electrical engineer could have. It impacts all modern aspects of life and sciences; from communication, entertainment to health and economics. https://academictorrents.com/download/530416f4f3a4b2cac90e61d5df72d1610dec68b4 Integrative Biology 131: General Human Anatomy. Fall 2005. Professor Marian Diamond. The functional anatomy of the human body as revealed by gross and microscopic examination. The Department of Integrative Biology offers a program of instruction that focuses on the integration of structure and function in the evolution of diverse biological systems. It investigates integration at all levels of organization from molecules to the biosphere, and in all taxa of organisms from viruses to higher plants and animals. https://academictorrents.com/download/5a0d6b38ab0adb9e52182164ffa8db19822f73ef @article{, title = {[Coursera] VLSI CAD: Logic to Layout (University of Illinois at Urbana-Champaign) (vlsicad)}, author = {University of Illinois at Urbana-Champaign} } https://academictorrents.com/download/ec1c86afefda42f4b36c34ae7b235ef0bfd6b9d3 @article{, title = {[Coursera] Sports and Building Aerodynamics (Eindhoven University of Technology) (spobuildaerodynamics)}, author = {Eindhoven University of Technology} } https://academictorrents.com/download/3ba301f087680f41a88224c49c01218b24de868b @article{, title = {[Coursera] Social Network Analysis (University of Michigan) (sna)}, author = {University of Michigan} } https://academictorrents.com/download/066a55d231d3918ad3de994e6211bb99417bcdf0 @article{, title= {[Coursera] Scientific Computing (University of Washington) (scientificcomp)}, author= {University of Washington}, keywords= {Coursera, scientificcomp}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/6f7e43052129b95f470d3043cfce2bf5c15ae380 @article{, title= {[Coursera] High Performance Scientific Computing (University of Washington) (scicomp)}, author= {University of Washington}, keywords= {Coursera, scicomp}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/cb91a3d7a4c4c086be240b54e83ed8d587b31ff5 @article{, title = {[Coursera] Recommender Systems (University of Minnesota) (recsys)}, author = {University of Minnesota} } https://academictorrents.com/download/42c3b47bf15a2a1aaadf92156f19315ad2d22967 @article{, title = {[Coursera] Discrete Optimization (The University of Melbourne) (optimization)}, author = {The University of Melbourne} } https://academictorrents.com/download/ed196d080a2208727a225ab5e7a5630e5bf53be4 @article{, title = {[Coursera] Introduction to Natural Language Processing (University of Michigan) (nlpintro)}, author = {University of Michigan} } https://academictorrents.com/download/78515f90de063ffc144be5e7e726c03849b4e0ed @article{, title= {[Coursera] Natural Language Processing (Stanford University) (nlp)}, author= {Stanford University}, keywords= {Coursera, nlp}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/d2c8f8f1651740520b7dfab23438d89bc8c0c0ab @article{, title= {[Coursera] Natural Language Processing (Columbia University) (nlangp)}, author= {Columbia University}, keywords= {Coursera, nlangp}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/8a8f93e18dd6c46c48ee2936ed500b1ff4cc9175 @article{, title = {[Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)}, author = {University of Toronto} } https://academictorrents.com/download/3e6f1876bbd46780602e72f4b122329fb668bd2c @article{, title = {[Coursera] Nanotechnology: The Basics (Rice University) (nanotech)}, author = {Rice University} } https://academictorrents.com/download/5d0746961dc72f9ec4a9cc4cd07b1a4e01349fb4 @article{, title = {[Coursera] Fundamentals of Music Theory (The University of Edinburgh) (musictheory)}, author = {The University of Edinburgh} } https://academictorrents.com/download/8033015783d2df3e8b33a343edb8cea9a0b8319a @article{, title = {[Coursera] MRI Fundamentals (Korea Advanced Institute of Science and Technology) (mrifundamentals)}, author = {Korea Advanced Institute of Science and Technology} } https://academictorrents.com/download/77d3798548f7ea65943c4b08bf3167329c18fa49 @article{, title= {[Coursera] Model Thinking (University of Michigan) (modelthinking)}, author= {University of Michigan}, keywords= {Coursera, modelthinking}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/05a8fe3f7e3420df6b83f40b0cbccd05e591d9f4 @article{, title = {[Coursera] Mining Massive Datasets (Stanford University) (mmds)}, author = {Stanford University} } https://academictorrents.com/download/91bc48e6c8341de198c970acccdc87199391ab46 @article{, title= {[Coursera] Machine Learning (Stanford University) (ml)}, author= {Stanford University}, keywords= {Coursera, ml}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/e8b1f9c5bf555fe58bc73addb83457dd6da69630 @article{, title = {[Coursera] Mathematical Methods for Quantitative Finance (University of Washington) (mathematicalmethods)}, author = {University of Washington} } https://academictorrents.com/download/de1360e53beb1ee13c3285af9bb232109fa168a1 @article{, title = {[Coursera] Logic: Language and Information 1 (The University of Melbourne) (logic1)}, author = {The University of Melbourne} } https://academictorrents.com/download/aa0902f96da833cc7f4ec70859a529276f5032d9 @article{, title = {[Coursera] Introduction to Philosophy (The University of Edinburgh) (introphil)}, author = {The University of Edinburgh} } https://academictorrents.com/download/8dcd401c1b3db696fc2f04d3b49c850f0e5cc309 @article{, title = {[Coursera] Introduction to Logic (Stanford University) (intrologic)}, author = {Stanford University} } https://academictorrents.com/download/0342ad0bd7ef06eb1500b5e7c8ef398060827c4e @article{, title = {[Coursera] The Hardware/Software Interface (University of Washington) (hwswinterface)}, author = {University of Washington} } https://academictorrents.com/download/b63a566df824b39740eb9754e4fe4c0140306f4b @article{, title = {[Coursera] Heterogeneous Parallel Programming (University of Illinois at Urbana-Champaign) (hetero)}, author = {University of Illinois at Urbana-Champaign} } https://academictorrents.com/download/de34574326abc4666c7ede41d0205a4a2129bf85 @article{, title = {[Coursera] Genomic and Precision Medicine (University of California, San Francisco) (genomicmedicine)}, author = {University of California, San Francisco} } https://academictorrents.com/download/c4b2f1ba8fb0ee0b8e49609d2b9c86efae36dc36 @article{, title = {[Coursera] Genes and the Human Condition (From Behavior to Biotechnology) (University of Maryland, College Park) (genes)}, author = {University of Maryland, College Park} } https://academictorrents.com/download/5aecd4978d7be3568f454fa6384dcd5683564f63 @article{, title = {[Coursera] Game Theory II: Advanced Applications (Stanford University) (gametheory2)}, author = {Stanford University} } https://academictorrents.com/download/fc641711af26384dbace511fa236a6e4dcb9f36c @article{, title = {[Coursera] Game Theory (Stanford University & The University of British Columbia) (gametheory)}, author = {Stanford University & The University of British Columbia} } https://academictorrents.com/download/7b96f8f76c4af7752f35c4fc26607cf50b6bb195 @article{, title = {[Coursera] Exploring Quantum Physics (University of Maryland, College Park) (eqp)}, author = {University of Maryland, College Park} } https://academictorrents.com/download/5261e17c70036651d1f83a6ca66c399da33bb46e @article{, title= {[Coursera] Understanding Einstein: The Special Theory of Relativity (Stanford University) (einstein)}, author= {Stanford University}, keywords= {Coursera, Einstein}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/be19083019ae3954680733d394e5e5b5b3572a15 @article{, title= {[Coursera] Principles of Economics for Scientists (Caltech) (econ1scientists)}, author= {Caltech}, keywords= {Coursera, econ1scientists}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/a0af8db9e7b924993ee017e492195b3eb1b0a78b @article{, title= {[Coursera] Digital Signal Processing (École Polytechnique Fédérale de Lausanne) (dsp)}, author= {École Polytechnique Fédérale de Lausanne}, keywords= {Coursera, dsp}, abstract= {}, terms= {} } https://academictorrents.com/download/43d881e5128841876104742314ccd9851901f460 @article{, title = {[Coursera] Introduction to Data Science (University of Washington) (datasci)}, author = {University of Washington} } https://academictorrents.com/download/1448261dd6932e549ba4a86b5d6750aae858d003 @article{, title = {[Coursera] Galaxies and Cosmology (Caltech) (cosmo)}, author = {Caltech} } https://academictorrents.com/download/b7d15931742718f330243dc0aeb110136c86359f @article{, title = {[Coursera] Computational Neuroscience (University of Washington) (compneuro)}, author = {University of Washington} } https://academictorrents.com/download/d180bcd510aeec3a20044a0946ac658b9ab30760 @article{, title= {[Coursera] Computational Methods for Data Analysis (University of Washington) (compmethods)}, author= {University of Washington}, keywords= {Coursera, compmethods}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/4281ef52a65d26489e686a0540d86abd4161b88e @article{, title= {[Coursera] Compilers (Stanford University) (compilers)}, author= {Stanford University}, keywords= {Coursera, compilers}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/b7579be97c2f01e4efadb0b6b06f0d071afeaac9 @article{, title= {[Coursera] Introduction to Computational Finance and Financial Econometrics (University of Washington) (compfinance)}, author= {University of Washington}, keywords= {Coursera, compfinance}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/f07203f2eedb4792c351ba0e28406dab9ab54d7d @article{, title= {[Coursera] Computer Architecture (Princeton University) (comparch)}, author= {Princeton University}, keywords= {Coursera, comparch}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/10d1bf7161a1b3ea70697cd61834ceea6c3d1f87 @article{, title = {[Coursera] Bitcoin and Cryptocurrency Technologies (Princeton University) (bitcointech)}, author = {Princeton University} } https://academictorrents.com/download/412d52b0bfcf2a8bf3201a28c2ba04b6dff5b290 @article{, title = {[Coursera] Bioinformatics: Life Sciences on Your Computer (Johns Hopkins University) (bioinform)}, author = {Johns Hopkins University} } https://academictorrents.com/download/b02188bbb764f7f5fdd499c5144add35f56ed3e7 @article{, title = {[Coursera] The Caltech-JPL Summer School on Big Data Analytics (Caltech) (bigdataschool)}, author = {Caltech} } https://academictorrents.com/download/71268d279bcfcd9d88c8989c72158d8d73a2e2fc @article{, title = {[Coursera] Web Intelligence and Big Data (Indian Institute of Technology Delhi) (bigdata)}, author = {Indian Institute of Technology Delhi} } https://academictorrents.com/download/8921ec4d2076e8a6e56a2387d5157aa7c0ef7f10 @article{, title = {[Coursera] Automata (Stanford University) (automata)}, author = {Stanford University} } https://academictorrents.com/download/459e24d28a6abce04cc9fd6e9a148c86dcaac19c @article{, title = {[Coursera] Astrobiology and the Search for Extraterrestrial Life (The University of Edinburgh) (astrobio)}, author = {The University of Edinburgh} } https://academictorrents.com/download/47d9877fa4f33d109721c65d066a26c3c5e12e0d @article{, title = {[Coursera] Analysis of Algorithms (Princeton University) (aofa)}, author = {Princeton University} } https://academictorrents.com/download/31939517fc774120c37160f93a9b5c73cf6c3271 @article{, title = {[Coursera] Algorithms, Part II (Princeton University) (algs4partII)}, author = {Princeton University} } https://academictorrents.com/download/5c22cd3a2f65f18e153faefda9730b51b21f6521 @article{, title = {[Coursera] Algorithms, Part I (Princeton University) (algs4partI)}, author = {Princeton University} } https://academictorrents.com/download/43534d22aea22778efce768c4304d6809fa58e6b @article{, title= {[Coursera] Algorithms: Design and Analysis, Part 2 (Stanford University) (algo2)}, author= {Stanford University}, keywords= {Coursera, algo2}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/e24c15ce89cac9c380284595d1d8a475cb485e28 @article{, title= {[Coursera] Algorithms: Design and Analysis, Part 1 (Stanford University) (algo)}, author= {Stanford University}, keywords= {Coursera, algo}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/7bfcfbaf2c53588b23ba1ebccae47a2b9c5197b7 @article{, title = {[Coursera] Artificial Intelligence Planning (The University of Edinburgh) (aiplan)}, author = {The University of Edinburgh} } https://academictorrents.com/download/560d07faaf09f640fea96b3650874e2903cbc639 @article{, title= {[Coursera ] Text Mining and Analytics}, keywords= {text mining, analytics}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/e2c129491a3841bfac5d7b08b41ad79387132a23 @article{, title= {Biology-1B-Spring2015-UCBerkeley}, keywords= {}, journal= {}, author= {}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/6858d4831e4b76752097772ca9850b2ae3174e25 https://academictorrents.com/download/256d7b478f5c95275031e2d38a101091c0a9f5d1 The MIT Biology Department core courses, 7.012, 7.013, and 7.014, all cover the same core material, which includes the fundamental principles of biochemistry, genetics, molecular biology, and cell biology. Biological function at the molecular level is particularly emphasized and covers the structure and regulation of genes, as well as, the structure and synthesis of proteins, how these molecules are integrated into cells, and how these cells are integrated into multicellular systems and organisms. In addition, each version of the subject has its own distinctive material. 7.012 focuses on the exploration of current research in cell biology, immunology, neurobiology, genomics, and molecular medicine. Course Highlights This course features a complete set of video lectures by Professor Eric Lander, Director of the Broad Institute at MIT and a principal leader of the Human Genome Project and Professor Robert A. Weinberg, winner of the 1997 National Medal of Science. Education development efforts for these introductory biology courses are one of many activities conducted by the HHMI Education Group Group at MIT. This group focuses on curriculum development work for creating teaching tools in undergraduate biology courses. | LEC # | TOPICS | KEY DATES | |————————————————-|———————————————————————-|—————————-| | 1 | Introduction | | | 2 | Biochemistry 1 | | | 3-5 | Biochemistry 2 | | | | | | | Biochemistry 3 | | | | | | | | Biochemistry 4 | Problem set 1 due in lecture 5 | | | 6 | Genetics 1 | | | 7 | Genetics 2 | | | 8 | Genetics 3 | Problem set 2 due | | 9 | Human Genetics | | | 10 | Molecular Biology 1 | | | 11 | Molecular Biology 2 | | | Quiz 1, Lectures 1-10 | | | | 12 | Molecular Biology 3 | | | 13 | Gene Regulation | | | 14 | Protein Localization | | | 15-18 | Recombinant DNA 1 | | | | | | | Recombinant DNA 2 | | | | | | | | Recombinant DNA 3 | | | | | | | | Recombinant DNA 4 | Problem set 3 due in lecture 15 | | | | | | | Problem set 4 due in lecture 18 | | | | Quiz 2, Lectures 11-17 | | | | 19 | Cell Cycle/Signaling | | | 20 | Cancer | | | 21 | Virology/Tumor Viruses | | | 22-23 | Immunology 1 | | | | | | | Immunology 2 | Problem set 5 due in lecture 23 | | | 24 | AIDS | | | 25 | Genomics | | | Quiz 3, Lectures 18-24 | | | | 26 | Nervous System 1 | | | 27 | Nervous System 2 | | | 28 | Nervous System 3 | | | 29-30 | Stem Cells/Cloning 1 | | | | | | | Stem Cells/Cloning 2 | | | | 31 | Molecular Medicine 1 | | | 32 | Molecular Evolution | | | 33 | Molecular Medicine 2 | Problem set 6 due | | 34 | Human Polymorphisms and Cancer Classification | | | 35 | Future of Biology | | https://academictorrents.com/download/7e1ba0f77f65f5e43e3147691da2ddc36330a361 ### Course Description This course provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Cellular systems include genetic switches and oscillators, network motifs, genetic network evolution, and cellular decision-making. Population-level systems include models of pattern formation, cell-cell communication, and evolutionary systems biology. ### Prerequisites Given the wide range of backgrounds among students in this class we will try to avoid unnecessary jargon and mathematics. However, it will be very helpful if you are comfortable with the material in Introductory Biology 7.012, Differential Equations 18.03, and Probability 18.05. In addition, each weekly problem set will have a computational problem, so prior experience with a computational package such as MATLAB®, Mathematica®, or Python is expected. The "officially supported" package will be Python (sample code, etc), but problems can be done in any language. ### Textbooks Required Textbook Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall / CRC, 2006. ISBN: 9781584886426. [Preview with Google Books] Nowak, M. A. Evolutionary Dynamics: Exploring the Equations of Life. Belknap Press, 2006. ISBN: 9780674023383. [Preview with Google Books] Supplementary Reading Alberts, Bruce. Essential Cell Biology. Garland Science, 2009. ISBN: 9780815341291. Strogatz, Steven H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Westview Press, 2014. ISBN: 9780813349107. [Preview with Google Books] | LEC # | TOPICS | KEY DATES | |———-|———————————————————————————————————————————————————|——————————| | 1 | Introduction to the class and overview of topics. Basic concepts in networks and chemical reactions. | | | 2 | Input function of a gene, Michaelis-Menten kinetics, and cooperativity | | | 3 | Autoregulation, feedback and bistability | Problem Set 1 due | | 4 | Introduction to synthetic biology and stability analysis in the toggle switch | | | 5 | Oscillatory genetic networks | Problem Set 2 due | | 6 | Graph properties of transcription networks | | | 7 | Feed-forward loop network motif | Problem Set 3 due | | 8 | Introduction to stochastic gene expression | | | 9 | Causes and consequences of stochastic gene expression | Problem Set 4 due | | 10 | Stochastic modeling—The master equation, Fokker-Planck Equation, and the Gillespie algorithm | | | 11 | Life at low Reynold’s number | Problem Set 5 due | | 12 | Robustness and bacterial chemotaxis | | | | No Lecture | Midterm 1 | | 13 | Robustness in development and pattern formation | Problem Set 6 due | | 14 | Introduction to microbial evolution experiments, and optimal gene circuit design | | | 15 | Evolution in finite populations, genetic drift, and the theory of neutral molecular evolution | Problem Set 6 due | | 16 | Clonal interference and the distribution of beneficial mutations | | | 17 | Fitness landscapes and sequence spaces | Problem Set 7 due | | 18 | Evolutionary games | | | | No Lecture | Midterm 2 | | 19 | Survival in fluctuating environments | Problem Set 8 due | | 20 | Parasites, the evolution of virulence and sex | | | 21 | Interspecies interactions, the Lotka-Volterra model, and predator-prey oscillations | Problem Set 9 due | | 22 | Ecosystem stability, critical transitions, and the maintenance of biodiversity | | | 23 | Dynamics of populations in space | Problem Set 10 due | | 24 | The neutral theory of ecology | | | | | Final Exam | Instructor(s) Prof. Jeff Gore MIT Course Number 8.591J / 7.81J / 7.32 As Taught In Fall 2014 Level Undergraduate / Graduate https://academictorrents.com/download/98961a881e8fd4139f4d1e09f0b7a4badfab7c9d The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB Ph.D program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology. ### Course Description This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science, but not necessarily both. The scripting language Python—which is widely used for bioinformatics and computational biology—will be used; foundational material covering basic programming skills will be provided by the teaching assistants. Graduate versions of the course involve an additional project component. ### Prerequisites There are different prerequisites for the various versions of the course. See the table for clarification. 7.01 Fundamentals of Biology 7.05 General Biochemistry 5.07 Biological Chemistry 6.00 Introduction to Computer Science and Programming 6.01 Introduction to Electrical Engineering and Computer Science 18.440 Probability and Random Variables 6.041 Probabilistic Systems Analysis and Applied Probability | SES # | TOPICS | LECTURERS | KEY DATES | |———————————————————|—————————————————————————————————————————————————————————————————————-|——————|————————————————| | L1 | Course Introduction: History of Computational Biology; Overview of the Course; Course Policies and Mechanics; DNA Sequencing Technologies | CB, DG, EF | | | Genomic Analysis | | | | | L2 | Local Alignment (BLAST) and Statistics | CB | | | R1 | Statistics; Significance Testing; Bonferroni Correction | TA | | | L3 | Global Alignment of Protein Sequences (NW, SW, PAM, BLOSUM) | CB | Project: Interests Due | | L4 | Comparative Genomic Analysis of Gene Regulation | CB | | | R2 | Clustering, Model Selection, and BIC Scores | TA | | | Genomic Analysis—Next Gen Sequencing | | | | | L5 | Library Complexity and Short Read Alignment (Mapping) | DG | Problem Set 1 Due | | R3 | Burrows–Wheeler Transform (BWT) and Alignments. Guest Lecture: Heng Li (Broad Institute) | GL | | | L6 | Genome Assembly | DG | Project: Teams Due | | L7 | ChIP-seq Analysis; DNA-protein Interactions | DG | | | R4 | Simultaneous ChIP-seq Peak Discovery and Motif Sampling | TA | | | L8 | RNA-sequence Analysis: Expression, Isoforms | DG | | | Modeling Biological Function | | | | | L9 | Modeling and Discovery of Sequence Motifs (Gibbs Sampler, Alternatives) | CB | | | R5 | Gene Expression Program Discovery Using Topic Models | TA | | | L10 | Markov and Hidden Markov Models of Genomic and Protein Features | CB | | | L11 | RNA Secondary Structure—Biological Functions and Prediction | CB | Problem Set 2 Due | | R6 | Probabilistic Grammatical Models of RNA Structure | TA | | | E1 | Exam 1 | | | | Proteomics | | | | | L12 | Introduction to Protein Structure; Structure Comparison and Classification | EF | | | R7 | Protein Amino Acid Sidechain Packing Using Markov Random Fields | TA | Project: Research Strategy Due | | L13 | Predicting Protein Structure | EF | | | L14 | Predicting Protein Interactions | EF | Problem Set 3 Due | | R8 | Protein / Protein Interaction Prediction Using Threading | TA | | | Regulatory Networks | | | | | L15 | Gene Regulatory Networks | EF | | | L16 | Protein Interaction Networks | EF | | | R9 | Regression Trees | TA | | | L17 | Logic Modeling of Cell Signaling Networks. Guest Lecture: Doug Lauffenburger | GL | | | L18 | Analysis of Chromatin Structure | DG | Problem Set 4 Due | | R10 | BayesNets | TA | | | Computational Genetics | | | | | L19 | Discovering Quantitative Trait Loci (QTLs) | DG | Project: Written Report Due | | R11 | Narrow Sense Heritability | TA | | | L20 | Human Genetics, SNPs, and Genome Wide Associate Studies | DG | | | L21 | Synthetic Biology: From Parts to Modules to Therapeutic Systems. Guest Lecture: Ron Weiss | GL | Problem Set 5 Due | | R12 | Exam Review | TA | | | E2 | Exam 2 | | | | L22 | Causality, Natural Computing, and Engineering Genomes. Guest Lecture: George Church | GL | | | P1 | Presentations | | | | P2 | Presentations (cont.) | | | ### Instructor(s) Prof. Christopher Burge Prof. David Gifford Prof. Ernest Fraenkel MIT Course Number 7.91J / 20.490J / 20.390J / 7.36J / 6.802J / 6.874J / HST.506J As Taught In Spring 2014 Level Undergraduate / Graduate https://academictorrents.com/download/90435db14989b415561ef53a3a12657a13d9d9fa The last three or four decades have seen a remarkable evolution in the institutions that comprise the modern monetary system. The financial crisis of 2007-2009 is a wakeup call that we need a similar evolution in the analytical apparatus and theories that we use to understand that system. Produced and sponsored by the Institute for New Economic Thinking, this course is an attempt to begin the process of new economic thinking by reviving and updating some forgotten traditions in monetary thought that have become newly relevant. Three features of the new system are central. Most important, the intertwining of previously separate capital markets and money markets has produced a system with new dynamics as well as new vulnerabilities. The financial crisis revealed those vulnerabilities for all to see. The result was two years of desperate innovation by central banking authorities as they tried first this, and then that, in an effort to stem the collapse. Second, the global character of the crisis has revealed the global character of the system, which is something new in postwar history but not at all new from a longer time perspective. Central bank cooperation was key to stemming the collapse, and the details of that cooperation hint at the outlines of an emerging new international monetary order. Third, absolutely central to the crisis was the operation of key derivative contracts, most importantly credit default swaps and foreign exchange swaps. Modern money cannot be understood separately from modern finance, nor can modern monetary theory be constructed separately from modern financial theory. That s the reason this course places dealers, in both capital markets and money markets, at the very center of the picture, as profit-seeking suppliers of market liquidity to the new system of market-based credit. https://academictorrents.com/download/62fe3cfdec4d19d1d3bf98184b12538a46b783f5 Participants will learn how to move efficiently from patient signs and symptoms to a rational and prioritized set of diagnostic possibilities and will learn how to study and read to facilitate this process. Clinical problem solving or diagnostic reasoning is the skill that physicians use to understand a patient’s complaints and then to identify a short, prioritized list of possible diagnoses that could account for those complaints. This differential diagnosis then drives the choice of diagnostic tests and possible treatments. Despite striking advances in information technology, clinical problem solving has not yet been effectively replicated by computers, making it essential that clinicians work to develop expertise in this very important skill set. This course will examine the ways physicians think about clinical problem solving and will help participants develop competence in the building blocks of clinical problem solving. The professor will use cases to illustrate different reasoning strategies and will discuss how both correct and incorrect diagnoses result from these strategies. Participants will use sample clinical cases to practice what they have learned through the lectures. Finally, the professor will discuss strategies to help students and young physicians read textbooks and articles in a way that enhances their ability to use information in the clinical environment. https://academictorrents.com/download/dae02888e2fb6484a7b471cb7977eb859aba4831 In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques. Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations. https://academictorrents.com/download/e74f08f0fc699e84a9eb046309727d07d80171c5 An introduction to quantum physics with emphasis on topics at the frontiers of research, and developing understanding through exercise. Quantum physics is the foundation for much of modern technology, provides the framework for understanding light and matter from the subatomic to macroscopic domains, and makes possible the most precise measurements ever made. More than just a theory, it offers a way of looking at the world that grows richer with experience and practice. Our course will provide some of that practice and teach you "tricks of the trade" (not found in textbooks) that will enable you to solve quantum-mechanical problems yourself and understand the subject at a deeper level. The basic principles of quantum physics are actually quite simple, but they lead to astonishing outcomes. Two examples that we will look at from various perspectives are the prediction of the laser by Albert Einstein in 1917 and the prediction of antimatter by Paul Dirac in 1928. Both of these predictions came from very simple arguments in quantum theory, and led to results that transformed science and society. Another familiar phenomenon, magnetism, had been known since antiquity, but only with the advent of quantum physics was it understood how magnets worked, to a degree that made possible the discovery in the 1980’s of ultrastrong rare-earth magnets. However, lasers, antimatter and magnets are areas of vibrant research, and they are all encountered in the new field of ultracold atomic physics that will provide much of the material of “Exploring Quantum Physics”. Richard Feynman once said, “I think I can safely say that nobody understands quantum mechanics.” We say, that’s no reason not to try! What Feynman was referring to are some of the “spooky” phenomena like quantum entanglement, which are incomprehensible from the standpoint of classical physics. Even though they have been thoroughly tested by experiment, and are even being exploited for applications such as cryptography and logic processing, they still seem so counterintuitive that they give rise to extraordinary ideas such as the many-world theory. Quantum physics combines a spectacular record of discovery and predictive success, with foundational perplexities so severe that even Albert Einstein came to believe that it was wrong. This is what makes it such an exciting area of science! https://academictorrents.com/download/f24122f15283757aa8a9bf9cb638db266273442d This course introduces concepts, languages, techniques, and patterns for programming heterogeneous, massively parallel processors. Its contents and structure have been significantly revised based on the experience gained from its initial offering in 2012. It covers heterogeneous computing architectures, data-parallel programming models, techniques for memory bandwidth management, and parallel algorithm patterns. All computing systems, from mobile to supercomputers, are becoming heterogeneous, massively parallel computers for higher power efficiency and computation throughput. While the computing community is racing to build tools and libraries to ease the use of these systems, effective and confident use of these systems will always require knowledge about low-level programming in these systems. This course is designed for students to learn the essence of low-level programming interfaces and how to use these interfaces to achieve application goals. CUDA C, with its good balance between user control and verboseness, will serve as the teaching vehicle for the first half of the course. Students will then extend their learning into closely related programming interfaces such as OpenCL, OpenACC, and C++AMP. The course is unique in that it is application oriented and only introduces the necessary underlying computer science and computer engineering knowledge for understanding. It covers the concept of data parallel execution models, memory models for managing locality, tiling techniques for reducing bandwidth consumption, parallel algorithm patterns, overlapping computation with communication, and a variety of heterogeneous parallel programming interfaces. The concepts learned in this course form a strong foundation for learning other types of parallel programming systems. https://academictorrents.com/download/8903d0871c652b96c7b29db738cea76902d65888 This course provides you with opportunities to integrate and apply your information security knowledge. Following the case-study approach, you will be introduced to current, real-world cases developed and presented by the practitioner community. You will design and execute information assurance strategies to solve these cases. https://academictorrents.com/download/55284a002672598923af36bc55f3205b42c93b00 When you take a digital photo with your phone or transform the image in Photoshop, when you play a video game or watch a movie with digital effects, when you do a web search or make a phone call, you are using technologies that build upon linear algebra. Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, information retrieval and web search. Linear algebra in turn is built on two basic elements, the matrix and the vector. In this class, you will learn the concepts and methods of linear algebra, and how to use them to think about problems arising in computer science. You will write small programs in the programming language Python to implement basic matrix and vector functionality and algorithms, and use these to process real-world data to achieve such tasks as: two-dimensional graphics transformations, face morphing, face detection, image transformations such as blurring and edge detection, image perspective removal, audio and image compression, searching within an image or an audio clip, classification of tumors as malignant or benign, integer factorization, error-correcting codes, secret-sharing, network layout, document classification, and computing Pagerank (Google s ranking method). https://academictorrents.com/download/54cd86f3038dfd446b037891406ba4e0b1200d5a Examines key computational abstraction levels below modern high-level languages. From Java/C to assembly programming, to basic processor and system organization. This course examines key computational abstraction levels below modern high-level languages; number representation, assembly language, introduction to C, memory management, the operating-system process model, high-level machine architecture including the memory hierarchy, and how high-level languages are implemented. We will develop students’ sense of “what really happens” when software runs — and that this question can be answered at several levels of abstraction, including the hardware architecture level, the assembly level, the C programming level and the Java programming level. The core around which the course is built is C, assembly, and low-level data representation, but this is connected to higher levels (roughly how basic Java could be implemented), lower levels (the general structure of a processor and the memory hierarchy), and the role of the operating system (but not how the operating system is implemented). https://academictorrents.com/download/f1384286c8581bffba11e378fdb37608e649d82a About this course: In this course, you will learn to design the computer architecture of complex modern microprocessors. ### Introduction, Instruction Set Architecture, and Microcode This lecture will give you a broad overview of the course, as well as the description of architecture, micro-architecture and instruction set architectures. ### Pipelining Review This lecture covers the basic concept of pipeline and two different types of hazards. ### Cache Review This lecture covers control hazards and the motivation for caches. ### Superscalar 1 This lecture covers cache characteristics and basic superscalar architecture. ### Superscalar 2 & Exceptions This lecture covers the common issues for superscalar architecture. ### Superscalar 3 This lecture covers different kinds of architectures for out-of-order processors. ### Superscalar 4 This lecture covers the common methods used to improve the performance of out-of-order processors including register renaming and memory disambiguation. ### VLIW 1 This lecture covers the basic concept of very long instruction word (VLIW) processors. ### VLIW2 This lecture covers the common methods used to improve VLIW performance. ### Branch Prediction This lecture covers the motivation and implementation of branch predictors. ### Advanced Caches 1 This lecture covers the advanced mechanisms used to improve cache performance. ### Advanced Caches 2 This lecture covers more advanced mechanisms used to improve cache performance. ### Memory Protection This lecture covers memory management and protection. ### Vector Processors and GPUs This lecture covers the vector processor and optimizations for vector processors. ### Multithreading This lecture covers different types of multithreading. ### Parallel Programming 1 This lecture covers the concepts of parallelism, consistency models, and basic parallel programming techniques. ### Parallel Programming 2 This lecture covers the solutions for the consistency problem in parallel programming. ### Small Multiprocessors This lecture covers the implementation of small multiprocessors. ### Multiprocessor Interconnect 1 This lecture covers the design of interconnects for a multiprocessor. ### Multiprocessor Interconnect 2 This lecture covers the design of interconnects for multiprocessor and network topology. ### Large Multiprocessors (Directory Protocols) This lecture covers the motivation and implementation of directory protocol used for coherence on large multiproccesors. https://academictorrents.com/download/53bae6d22f3b6e692673f9335e0a0198c1618426 About this course: Not all programs are created equal. In this course, we ll focus on writing quality code that runs correctly and efficiently. We ll design, code and validate our programs and learn how to compare programs that are addressing the same task. https://academictorrents.com/download/5d940b05a2097b5fcf2392916e6c4901743fb219 About this course: Learn how to think the way mathematicians do - a powerful cognitive process developed over thousands of years. The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancestors have developed over three thousand years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking. The course is offered in two versions. The eight-week-long Basic Course is designed for people who want to develop or improve mathematics-based, analytic thinking for professional or general life purposes. The ten-week-long Extended Course is aimed primarily at first-year students at college or university who are thinking of majoring in mathematics or a mathematically-dependent subject, or high school seniors who have such a college career in mind. The final two weeks are more intensive and require more mathematical background than the Basic Course. There is no need to make a formal election between the two. Simply skip or drop out of the final two weeks if you decide you want to complete only the Basic Course. https://academictorrents.com/download/2b5e5cc8c7414bc3b0f6974190065bc8c2f629dc About this course: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. https://academictorrents.com/download/7afeafb540f4ff63690f1a6517748341f6809516 About this course: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. ## Union−Find We illustrate our basic approach to developing and analyzing algorithms by considering the dynamic connectivity problem. We introduce the union−find data type and consider several implementations (quick find, quick union, weighted quick union, and weighted quick union with path compression). Finally, we apply the union−find data type to the percolation problem from physical chemistry. ## Analysis of Algorithms The basis of our approach for analyzing the performance of algorithms is the scientific method. We begin by performing computational experiments to measure the running times of our programs. We use these measurements to develop hypotheses about performance. Next, we create mathematical models to explain their behavior. Finally, we consider analyzing the memory usage of our Java programs. ## Stacks and Queues We consider two fundamental data types for storing collections of objects: the stack and the queue. We implement each using either a singly-linked list or a resizing array. We introduce two advanced Java features—generics and iterators—that simplify client code. Finally, we consider various applications of stacks and queues ranging from parsing arithmetic expressions to simulating queueing systems. ## Elementary Sorts We introduce the sorting problem and Java s Comparable interface. We study two elementary sorting methods (selection sort and insertion sort) and a variation of one of them (shellsort). We also consider two algorithms for uniformly shuffling an array. We conclude with an application of sorting to computing the convex hull via the Graham scan algorithm. ## Mergesort We study the mergesort algorithm and show that it guarantees to sort any array of n items with at most n lg n compares. We also consider a nonrecursive, bottom-up version. We prove that any compare-based sorting algorithm must make at least n lg n compares in the worst case. We discuss using different orderings for the objects that we are sorting and the related concept of stability. ## Quicksort We introduce and implement the randomized quicksort algorithm and analyze its performance. We also consider randomized quickselect, a quicksort variant which finds the kth smallest item in linear time. Finally, we consider 3-way quicksort, a variant of quicksort that works especially well in the presence of duplicate keys. ## Priority Queues We introduce the priority queue data type and an efficient implementation using the binary heap data structure. This implementation also leads to an efficient sorting algorithm known as heapsort. We conclude with an applications of priority queues where we simulate the motion of n particles subject to the laws of elastic collision. ## Elementary Symbol Tables We define an API for symbol tables (also known as associative arrays) and describe two elementary implementations using a sorted array (binary search) and an unordered list (sequential search). When the keys are Comparable, we define an extended API that includes the additional methods min, max floor, ceiling, rank, and select. To develop an efficient implementation of this API, we study the binary search tree data structure and analyze its performance. ## Balanced Search Trees In this lecture, our goal is to develop a symbol table with guaranteed logarithmic performance for search and insert (and many other operations). We begin with 2−3 trees, which are easy to analyze but hard to implement. Next, we consider red−black binary search trees, which we view as a novel way to implement 2−3 trees as binary search trees. Finally, we introduce B-trees, a generalization of 2−3 trees that are widely used to implement file systems. ## Geometric Applications of BSTs We start with 1d and 2d range searching, where the goal is to find all points in a given 1d or 2d interval. To accomplish this, we consider kd-trees, a natural generalization of BSTs when the keys are points in the plane (or higher dimensions). We also consider intersection problems, where the goal is to find all intersections among a set of line segments or rectangles. ## Hash Tables We begin by describing the desirable properties of hash function and how to implement them in Java, including a fundamental tenet known as the uniform hashing assumption that underlies the potential success of a hashing application. Then, we consider two strategies for implementing hash tables—separate chaining and linear probing. Both strategies yield constant-time performance for search and insert under the uniform hashing assumption. ## Symbol Table Applications We consider various applications of symbol tables including sets, dictionary clients, indexing clients, and sparse vectors. https://academictorrents.com/download/a2934d859a14c07a80092ab03552310838f66590 Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. Modern cameras recognize faces, and smart phones voice commands, cars can see and detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is softwares that are trained rather than programmed to solve a task. Among the variety of approaches to modern computational learning, we focus on regularization techniques, that are key to high- dimensional learning. Regularization methods allow to treat in a unified way a huge class of diverse approaches, while providing tools to design new ones. Starting from classical notions of smoothness, shrinkage and margin, the course will cover state of the art techniques based on the concepts of geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning, feature selection, structured prediction, multitask learning and model selection. Practical applications for high dimensional problems, in particular in computational vision, will be discussed. The classes will focus on algorithmic and methodological aspects, while trying to give an idea of the underlying theoretical underpinnings. Practical laboratory sessions will give the opportunity to have hands on experience. RegML is a 20 hours advanced machine learning course including theory classes and practical laboratory sessions. The course covers foundations as well as recent advances in Machine Learning with emphasis on high dimensional data and a core set techniques, namely regularization methods. In many respect the course is compressed version of the 9.520 course at MIT". | CLASS | DAY | TIME | SUBJECT | FILES | |———-|—————|———————-|———————————————————————————————————————————————————|————| | 1 | Mon 6/27 | 9:30 - 11:00 | Introduction to Statistical Machine Learning | Lect_1 | | 2 | Mon 6/27 | 11:30 - 13:00 | Tikhonov Regularization and Kernels | Lect_2 | | 3 | Mon 6/27 | 14:00 - 16:00 | Laboratory 1: Binary classification and model selection | Lab 1 | | 4 | Tue 6/28 | 9:30 - 11:00 | Early Stopping and Spectral Regularization | Lect_3 | | 5 | Tue 6/28 | 11:30 - 13:00 | Regularization for Multi-task Learning | Lect_4 | | 6 | Tue 6/28 | 14:00 - 16:00 | Laboratory 2: Spectral filters and multi-class classification | Lab 2 | | - | Wed 6/29 | 9:30 - 10:00 | Workshop: Federico Girosi - Health Analytics and Machine Learning | | | - | Wed 6/29 | 10:00 - 10:30 | Workshop: Massimiliano Pontil - A Class of Regularizers based on Optimal Interpolation | | | - | Wed 6/29 | 10:30 - 11:00 | Workshop: Gadi Geiger - Visual and Auditory Aspects of Perception in Developmental Dyslexia | | | - | Wed 6/29 | 11:00 - 11:30 | Coffee Break | | | - | Wed 6/29 | 11:30 - 12:00 | Workshop: Alessandro Verri - Extracting Biomedical Knowledge through Regularized Learning Techniques | | | - | Wed 6/29 | 12:00 - 12:30 | Workshop: Thomas Vetter - Learning the Appearance of Faces: Probabilistic Morphable Models | | | - | Wed 6/29 | Afternoon | Free | | | 7 | Thu 6/30 | 9:30 - 11:00 | Sparsity Based Regularization | Lect_5 | | 8 | Thu 6/30 | 11:30 - 13:00 | Structured Sparsity | Lect_6 | | 9 | Thu 6/30 | 14:00 - 16:00 | Laboratory 3: Sparsity-based learning | Lab 3 | | 10 | Fri 7/1 | 9:30 - 11:00 | Data Representation: Dictionary Learning | Lect_7 | | 11 | Fri 7/1 | 11:30 - 13:00 | Data Representation: Deep Learning | Lect_8 | https://academictorrents.com/download/493251615310f9b6ae1f483126292378137074cd Lecture 1 — Machine Learning: Introduction to Machine Learning, Linear Models for Regression Reading: Bishop, Chapter 1: sec. 1.1 - 1.5. and Chapter 3: sec. 1.1 - 1.3. Optional: Bishop, Chapter 2: Backgorund material; Hastie, Tibshirani, Friedman, Chapters 2 and 3. Lecture 2 — Bayesian Framework: Bayesian Linear Regression, Evidence Maximization. Linear Models for Classification. Reading: Bishop, Chapter 3: sec. 3.3 - 3.5. Chapter 4. Optional: Radford Neal s NIPS tutorial on Bayesian Methods for Machine Learning:. Also see Max Welling s notes on Fisher Linear Discriminant Analysis Lecture 3 — Classification Linear Models for Classification, Generative and Discriminative approaches, Laplace Approximation. Reading: Bishop, Chapter 4. Optional: Hastie, Tibshirani, Friedman, Chapter 4. Lecture 4 — Graphical Models: Bayesian Networks, Markov Random Fields Reading: Bishop, Chapter 8. Optional: Hastie, Tibshirani, Friedman, Chapter 17 (Undirected Graphical Models). MacKay, Chapter 21 (Bayesian nets) and Chapter 43 (Boltzmann mchines). Also see this paper on Graphical models, exponential families, and variational inference by M. Wainwright and M. Jordan, Foundations and Trends in Machine Learning Lecture 5 — Mixture Models and EM: Mixture of Gaussians, Generalized EM, Variational Bound. Reading: Bishop, Chapter 9. Optional: Hastie, Tibshirani, Friedman, Chapter 13 (Prototype Methods). MacKay, Chapter 22 (Maximum Likelihood and Clustering). Lecture 6 — Variational Inference Mean-Field, Bayesian Mixture models, Variational Bound. Reading: Bishop, Chapter 10. Optional: MacKay, Chapter 33 (Variational Inference). Lecture 7 - Sampling Methods Rejection Sampling, Importance sampling, M-H and Gibbs. Reading: Bishop, Chapter 11. Optional: MacKay, Chapter 29 (Monte Carlo Methods). Lecture 8 — Continuous Latent Variable Models PCA, FA, ICA, Deep Autoencders Reading: Bishop, Chapter 12. Optional: Hastie, Tibshirani, Friedman, Chapters 14.5, 14.7, 14.9 (PCA, ICA, nonlinear dimensionality reduction). MacKay, Chapter 34 (Latent Variable Models). Lecture 9 — Modeling Sequential Data HMMs, LDS, Particle Filters. Reading: Bishop, Chapter 13. https://academictorrents.com/download/deb96e8d1f88d9b3a09098ce27c986507ae97b5e @article{, title= {[Coursera] The Social Context of Mental Health and Illness (University of Toronto)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/ab9ba816268fd74292d888cfb3f413803a178b90 @article{, title= {[Coursera] Preventing Chronic Pain A Human Systems Approach (University of Minnesota)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/271e22f67c7bda33158f2d83f443728cd749cd42 @article{, title= {[Coursera] VLSI CAD: Logic to Layout by Rob A. Rutenbar (University of Illinois at Urbana-Champaign)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/625ae5f99f1cfdc2b8eb42577ca5271ad78967e0 @article{, title= {[Coursera] Greek and Roman Mythology (University of Pennsylvania)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/01212a89f842f5ad1364c7fa3a8c4c514e2b26ab @article{, title= {[Coursera] Beginning Game Programming with C# (University of Colorado System)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/0a7ba7e62821e488a0061751fdb81f4298733bea @article{, title= {[Coursera] - (Operating Systems) by Professor Chen Xiangqun (Peking University)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/3fca7db35ba4773d416cc6a297a196cec41fa9f4 @article{, title= {[Coursera] Nanotechnology: The Basics by Professor Vicki Colvin, Daniel Mittleman (Rice University)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/e54694787f54a7a0096d9b0f062d31a3a3925ae8 @article{, title= {[Coursera] Analysis of Algorithms by Robert Sedgewick (Princeton University)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/ebdac281fb3e9ed0a5c7abadcda3575f54c73a8f @article{, title= {[Coursera] Mathematical Methods for Quantitative Finance by Dr. Kjell Konis (University of Washington)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/dfc1ddde962101f00ef9764b91181bd6bb5c9e93 @article{, title= {[Coursera] How to Succeed in College by Dr. Jonathan Golding, Dr. Phil Kraemer (University of Kentucky)}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/956ac58723e9f39e766741a6ea794006d20807c3 @article{, title= {[Coursera] Web Intelligence and Big Data by Dr. Gautam Shroff}, keywords= {}, journal= {}, author= {Coursera}, year= {}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} } https://academictorrents.com/download/f871965e36ec21677d44375930d6cbb0dab7bfb3 Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems. https://academictorrents.com/download/dd9b74b50a1292b4b154094b7338ec1d66e8894d Course Description Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge. https://academictorrents.com/download/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7 Here is the list of topics covered in the course, segmented over 10 weeks. Each week is associated with explanatory video clips and recommended readings. 0. Introduction and math revision 1. Feedforward neural network 2. Training neural networks 3. Conditional random fields 4. Training CRFs 5. Restricted Boltzmann machine 6. Autoencoders 7. Deep learning 8. Sparse coding 9. Computer vision 10. Natural language processing https://academictorrents.com/download/e046bca3bc837053d1609ef33d623ee5c5af7300 Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, encapsulation, data structures, databases, memory management, security, software development, virtualization, and websites. Languages include C, PHP, and JavaScript plus SQL, CSS, and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Designed for concentrators and non-concentrators alike, with or without prior programming experience. https://academictorrents.com/download/ee6f0ba62a717e7785abb5a98e45e2a08699f6c6 8.02 Classical Theory of Electromagnetism. In addition to the basic concepts of Electromagnetism, a vast variety of interesting topics are covered in this course: Lightning, Pacemakers, Electric Shock Treatment, Electrocardiograms, Metal Detectors, Musical Instruments, Magnetic Levitation, Bullet Trains, Electric Motors, Radios, TV, Car Coils, Superconductivity, Aurora Borealis, Rainbows, Radio Telescopes, Interferometers, Particle Accelerators (a.k.a. Atom Smashers or Colliders), Mass Spectrometers, Red Sunsets, Blue Skies, Haloes around Sun and Moon, Color Perception, Doppler Effect, Big-Bang Cosmology. https://academictorrents.com/download/7010163eae33fbac6c065095e908c2c49550b931 ==Course Description This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm. More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms; the BCJR algorithm; turbo codes, LDPC codes and RA codes; and performance of LDPC codes with iterative decoding. Finally, the course addresses coding for the bandwidth-limited regime, including lattice codes, trellis-coded modulation, multilevel coding and shaping. If time permits, it covers equalization of linear Gaussian channels. Lecture 1: Introduction Sampling Theorem Lecture 2: Performance of Small Signal Constellations Lecture 3: Hard-decision and Soft-decision Decoding Lecture 4: Hard-decision and Soft-decision Decoding Lecture 5: Introduction to Binary Block Codes Lecture 6: Introduction to Binary Block Codes Lecture 7: Introduction to Finite Fields Lecture 8: Introduction to Finite Fields Lecture 9: Introduction to Finite Fields Lecture 10: Reed-Solomon Codes Lecture 11: Reed-Solomon Codes Lecture 12: Reed-Solomon Codes Lecture 13: Introduction to Convolutional Codes Lecture 14: Introduction to Convolutional Codes Lecture 15: Trellis Representations of Binary Linear Block Codes Lecture 16: Trellis Representations of Binary Linear Block Codes Lecture 17: Codes on Graphs Lecture 18: Codes on Graphs Lecture 19: The Sum-Product Algorithm Lecture 20: Turbo, LDPC, and RA Codes Lecture 21: Turbo, LDPC, and RA Codes Lecture 22: Lattice and Trellis Codes Lecture 23: Lattice and Trellis Codes Lecture 24: Linear Gaussian Channels Lecture 25: Linear Gaussian Channels https://academictorrents.com/download/83259dc0c7dbd50073113cb1aa7a3e574faca70c Catalog description Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance. Course objectives to give students the tools and training to recognize convex optimization problems that arise in applications to present the basic theory of such problems, concentrating on results that are useful in computation to give students a thorough understanding of how such problems are solved, and some experience in solving them to give students the background required to use the methods in their own research work or applications Videos 1. Introduction 2. Convex sets 3. Convex functions 4. Convex optimization problems 5. Duality 6. Approximation and fitting 7. Statistical estimation 8. Geometric problems 9. Numerical linear algebra background 10. Unconstrained minimization 11. Equality constrained minimization 12. Interior-point methods 13. Conclusions https://academictorrents.com/download/393dc896234b96a1cd251c14cfc65d2ff594d6e9 ##Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion: * What is learning? * Can a machine learn? * How to do it? * How to do it well? * Take-home lessons. Lecture 01 - The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 02 - Is Learning Feasible? Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 03 - The Linear Model I - Linear classification and linear regression. Extending linear models through nonlinear transforms. Lecture 04 - Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 05 - Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 06 - Theory of Generalization - How an infinite model can learn from a finite sample. The most important theoretical result in machine learning. Lecture 07 - The VC Dimension - A measure of what it takes a model to learn. Relationship to the number of parameters and degrees of freedom. Lecture 08 - Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities. The learning curves. Lecture 09 - The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent. Lecture 10 - Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 11 - Overfitting - Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise. Lecture 12 - Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay. Lecture 13 - Validation - Taking a peek out of sample. Model selection and data contamination. Cross validation. Lecture 14 - Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 15 - Kernel Methods - Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins. Lecture 16 - Radial Basis Functions - An important learning model that connects several machine learning models and techniques. Lecture 17 - Three Learning Principles - Major pitfalls for machine learning practitioners; Occam?s razor, sampling bias, and data snooping. Lecture 18 - Epilogue - The map of machine learning. Brief views of Bayesian learning and aggregation methods. https://academictorrents.com/download/8190b5122515ab158cd29ccdb33ea946a3e529f4 Course Highlights This course features a complete set of video lectures by Professor Graham Walker, a Howard Hughes Medical Institute (HHMI) professor and director of the HHMI Education group at MIT, and Professor Sallie W. Chisholm, Lee and Geraldine Martin Professor of Environmental Studies and co-director of the MIT Earth Systems Initiative. Education development efforts for these introductory biology courses are one of many activities conducted by the HHMI Education Group at MIT. This group focuses on curriculum development work for creating teaching tools in undergraduate biology courses. Course Description The MIT Biology Department core courses, 7.012, 7.013, and 7.014, all cover the same core material, which includes the fundamental principles of biochemistry, genetics, molecular biology, and cell biology. Biological function at the molecular level is particularly emphasized and covers the structure and regulation of genes, as well as, the structure and synthesis of proteins, how these molecules are integrated into cells, and how these cells are integrated into multicellular systems and organisms. In addition, each version of the subject has its own distinctive material. 7.014 focuses on the application of these fundamental principles, toward an understanding of microorganisms as geochemical agents responsible for the evolution and renewal of the biosphere and of their role in human health and disease. Acknowledgements The study materials, problem sets, and quiz materials used during Spring 2005 for 7.014 include contributions from past instructors, teaching assistants, and other members of the MIT Biology Department affiliated with course 7.014. Since the following works have evolved over a period of many years, no single source can be attributed. https://academictorrents.com/download/a2b8a74967e5166ab55bf5f248af8d564c39f795 ##Course Description Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE s) deal with functions of one variable, which can often be thought of as time. ##Prerequisites/Corequisites 18.03 Differential Equations has 18.01 Single Variable Calculus as a prerequisite. 18.02 Multivariable Calculus is a corequisite, meaning students can take 18.02 and 18.03 simultaneously. ##Texts Buy at Amazon Edwards, C., and D. Penney. Elementary Differential Equations with Boundary Value Problems. 6th ed. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 9780136006138. Note: The 5th Edition (Buy at Amazon ISBN: 9780131457744) will serve as well. Students also need two sets of notes "18.03: Notes and Exercises" by Arthur Mattuck, and "18.03 Supplementary Notes" by Haynes Miller. ##Description This course is a study of Ordinary Differential Equations (ODE s), including modeling physical systems. ##Topics include: * Solution of First-order ODE s by Analytical, Graphical and Numerical Methods; * Linear ODE s, Especially Second Order with Constant Coefficients; * Undetermined Coefficients and Variation of Parameters; * Sinusoidal and Exponential Signals: Oscillations, Damping, Resonance; * Complex Numbers and Exponentials; * Fourier Series, Periodic Solutions; * Delta Functions, Convolution, and Laplace Transform Methods; * Matrix and First-order Linear Systems: Eigenvalues and Eigenvectors; and * Non-linear Autonomous Systems: Critical Point Analysis and Phase Plane Diagrams. ##Format The lecture period is used to help students gain expertise in understanding, constructing, solving, and interpreting differential equations. * Students must come to lecture prepared to participate actively. At the first recitation, students are given a set of flashcards to bring to each lecture. They are used during class sessions to vote on answers to questions posed occasionally in the lecture. In case of divided opinions, a discussion follows. As a further element of active participation in class, students will often be asked to spend a minute responding to a short feedback question at the end of the lecture. ##Recitations These small groups meet twice a week to discuss and gain experience with the course material. Even more than the lectures, the recitations involve active participation. The recitation leader may begin by asking for questions or hand out problems to work on in small groups. Students are encouraged to ask questions early and often. Recitation leaders also hold office hours. ##Tutoring Another resource of great value to students is the tutoring room. This is staffed by experienced undergraduates. Extra staff is added before hour exams. This is a good place to go to work on homework. ##The Ten Essential Skills Students should strive for personal mastery over the following skills. These are the skills that are used in other courses at MIT. This list of skills is widely disseminated among the faculty teaching courses listing 18.03 as a prerequisite. At the moment, 140 courses at MIT list 18.03 as a prerequisite or a corequisite. Model a simple system to obtain a first order ODE. Visualize solutions using direction fields and isoclines, and approximate them using Euler s method. Solve a first order linear ODE by the method of integrating factors or variation of parameter. Calculate with complex numbers and exponentials. Solve a constant coefficient second order linear initial value problem with driving term exponential times polynomial. If the input signal is sinusoidal, compute amplitude gain and phase shift. Compute Fourier coefficients, and find periodic solutions of linear ODEs by means of Fourier series. Utilize Delta functions to model abrupt phenomena, compute the unit impulse response, and express the system response to a general signal by means of the convolution integral. Find the weight function or unit impulse response and solve constant coefficient linear initial value problems using the Laplace transform together with tables of standard values. Relate the pole diagram of the transfer function to damping characteristics and the frequency response curve. Calculate eigenvalues, eigenvectors, and matrix exponentials, and use them to solve first order linear systems. Relate first order systems with higher-order ODEs. Recreate the phase portrait of a two-dimensional linear autonomous system from trace and determinant. Determine the qualitative behavior of an autonomous nonlinear two-dimensional system by means of an analysis of behavior near critical points. The Ten Essential Skills is also available as a (PDF). ##Homework Each homework assignment has two parts: a first part drawn from the book or notes, and a second part consisting of problems which will be handed out. Both parts are keyed closely to the lectures. Students should form the habit of doing the relevant problems between successive lectures and not try to do the whole set the night before they are due. https://academictorrents.com/download/e7ef009c1929f074ea556bad005ddca0d99ac7aa Course Description ————————— 8.03 Classical theory of vibration and waves. In addition to the traditional topics of mechanical vibrations and waves, coupled oscillators, and electro-magnetic radiation, students will also learn about musical instruments, red sunsets, glories, coronae, rainbows, haloes, X-ray binaries, neutron stars, black holes and big-bang cosmology. Highlights of this Course ————————————- This course features a full set of lecture videos, as well as assignments, exams, and other course materials. This is a full set of video lectures, recorded at MIT. The home page of the original lectures can be found at MIT OCW. This torrent is just a transcode of the original 220 Kbps .rm files to mpeg4, 15 fps, 320 x 240 h.264 @ 144 Kbps, with 32 Kbps mono AAC sound, so that it can for example be easily played on a phone during your daily commute. https://academictorrents.com/download/724648552d517756117b47b3a7f5f62962f2629e This is a full set of video lectures, recorded at MIT. The home page of the original lectures can be found at MIT OCW: This torrent is just a transcode of the original 220 Kbps .rm files to mpeg4, 15 fps, 320 x 240 h.264 @ 144 Kbps, with 32 Kbps mono AAC sound, so that it can for example be easily played on a phone during your daily commute. Course description —————————- 8.01 is a first-semester freshman physics class in Newtonian Mechanics, Fluid Mechanics, and Kinetic Gas Theory. In addition to the basic concepts of Newtonian Mechanics, Fluid Mechanics, and Kinetic Gas Theory, a variety of interesting topics are covered in this course: Binary Stars, Neutron Stars, Black Holes, Resonance Phenomena, Musical Instruments, Stellar Collapse, Supernovae, Astronomical observations from very high flying balloons (lecture 35), and you will be allowed a peek into the intriguing Quantum World. Highlights of this Course ————————————- This course features lecture notes, problem sets with solutions, exams with solutions, links to related resources, and a complete set of videotaped lectures. The 35 video lectures by Professor Lewin, were recorded on the MIT campus during the Fall of 1999. Prof. Lewin is well-known at MIT and beyond for his dynamic and engaging lecture style. License ———- These lectures are generously put on-line by MIT, and are licensed under the Creative Commons License (BY-NC-SA) and so it is perfectly legal to share them. Therefore, please seed as long a possible, to ensure this amazing resource stays available. https://academictorrents.com/download/f231c62635aadfb0e4d1f45ddc7b5b6c5592b275 About the Course Democratic Development is intended as a broad, introductory survey of the political, social, cultural, economic, institutional, and international factors that foster or obstruct the development, and consolidation, of democracy. Topics will be examined in historical and comparative perspective, and reference a variety of different national experiences. It is hoped that students in developing or prospective democracies will use the theories, ideas, and lessons in the class to help build or improve democracy in their own countries. This course is primarily intended for individuals in college or beyond, with some academic background or preparation in political science or the social sciences. However, it seeks to be accessible and useful to a diverse international audience, including educators at the secondary and college levels, government officials, development professionals, civil society leaders, journalists, bloggers, activists, and individuals involved in a wide range of activities and professions related to the development and deepening of democracy. Course Syllabus Week 1 Introduction to the Course, Why Democracy? What Is Democracy? Regime Types The Third Wave of Democratization and its Ebb Week 2 Legitimacy, Authority and Effectiveness Democratic Consolidation Week 3 Political Culture and Democracy Are Democratic Values Universal? Week 4 Economic Development Class Structure and Inequality Civil Society Week 5 Democratic Transition: Paths and Drivers Democratic Transition: Types and Means Week 6 Constitutional Design Presidential vs. Parliamentary Government Parties and Party Systems Week 7 Electoral Systems Choosing between Different Systems Week 8 Ethnicity and Ethnic Conflict Managing Ethnic Conflict Federalism Week 9 Horizontal Accountability and the Rule of Law Controlling Corruption Democratic Breakdowns Week 10 International Factors Promoting Democracy Week 11 The Future of Democracy https://academictorrents.com/download/1452d6a906d5d164c1adfc4b81a74a6690a8ec4b