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MIT OCW 18.S191 Introduction to Computational Thinking (Fall 2022)

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Path	Size
Lecture 1 - Course Welcome + Intro to Arrays & Images1.en.vtt	92.92kB
Lecture 1 - Course Welcome + Intro to Arrays & Images1.mp4	234.54MB
Lecture 2 - Transforming Images.en.vtt	92.33kB
Lecture 2 - Transforming Images.mp4	241.05MB
Lecture 3 - Transformations & AutoDiff.en.vtt	90.85kB
Lecture 3 - Transformations & AutoDiff.mp4	208.17MB
Lecture 4 - Transformations 2 - Composability and Linearity.en.vtt	104.53kB
Lecture 4 - Transformations 2 - Composability and Linearity.mp4	224.54MB
Lecture 5 - Inverses and Newton method.en.vtt	99.43kB
Lecture 5 - Inverses and Newton method.mp4	213.25MB
Lecture 6 - Dynamic Programming and Seam Carving.en.vtt	105.24kB
Lecture 6 - Dynamic Programming and Seam Carving.mp4	152.26MB
Lecture 7 - Structure.en.vtt	90.17kB
Lecture 7 - Structure.mp4	187.57MB
Lecture 8 - Principal Component Analysis.en.vtt	91.51kB
Lecture 8 - Principal Component Analysis.mp4	197.45MB
Lecture 9 - Sampling and random variables.en.vtt	94.69kB
Lecture 9 - Sampling and random variables.mp4	179.83MB
Lecture 10 - Modeling with stochastic simulation.en.vtt	487.27kB
Lecture 10 - Modeling with stochastic simulation.mp4	156.72MB
Lecture 11 - Random variables as types.en.vtt	87.64kB
Lecture 11 - Random variables as types.mp4	207.41MB
Lecture 12 - Random walks I.en.vtt	98.35kB
Lecture 12 - Random walks I.mp4	168.17MB
Lecture 13 - Random walks II.en.vtt	97.56kB
Lecture 13 - Random walks II.mp4	202.11MB
Lecture 14 - Discrete & Continuous.en.vtt	87.21kB
Lecture 14 - Discrete & Continuous.mp4	219.43MB
Lecture 15 - Linear models and simulations.en.vtt	89.62kB
Lecture 15 - Linear models and simulations.mp4	123.72MB
Lecture 16 - Optimization.en.vtt	81.78kB
Lecture 16 - Optimization.mp4	120.68MB
Lecture 17 - Time stepping and differential equations.en.vtt	95.19kB
Lecture 17 - Time stepping and differential equations.mp4	194.01MB
Lecture 18 - Libraries & parameterized types.en.vtt	97.06kB
Lecture 18 - Libraries & parameterized types.mp4	222.85MB
Lecture 19 - Why can't we predict the weather？.en.vtt	93.91kB
Lecture 19 - Why can't we predict the weather？.mp4	200.84MB
Lecture 20 - A first climate model.en.vtt	85.14kB
Lecture 20 - A first climate model.mp4	246.64MB
Lecture 21 - How to collaborate on software.en.vtt	91.58kB
Lecture 21 - How to collaborate on software.mp4	230.08MB
Lecture 22 - Snowball Earth and hysteresis.en.vtt	85.49kB
Lecture 22 - Snowball Earth and hysteresis.mp4	194.33MB
Lecture 23 - Advection and diffusion - PDEs in 1D.en.vtt	86.36kB
Lecture 23 - Advection and diffusion - PDEs in 1D.mp4	165.31MB
Lecture 24 - Resistors, stencils and climate models.en.vtt	106.37kB
Lecture 24 - Resistors, stencils and climate models.mp4	182.91MB
Lecture 25 - Modeling climate change.en.vtt	97.54kB
Lecture 25 - Modeling climate change.mp4	237.61MB

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MIT OCW 18.S191 Introduction to Computational Thinking (Fall 2022) (50 files)

Lecture 25 - Modeling climate change.mp4	237.61MB
Lecture 25 - Modeling climate change.en.vtt	97.54kB
Lecture 24 - Resistors, stencils and climate models.mp4	182.91MB
Lecture 24 - Resistors, stencils and climate models.en.vtt	106.37kB
Lecture 23 - Advection and diffusion - PDEs in 1D.mp4	165.31MB
Lecture 23 - Advection and diffusion - PDEs in 1D.en.vtt	86.36kB
Lecture 22 - Snowball Earth and hysteresis.mp4	194.33MB
Lecture 22 - Snowball Earth and hysteresis.en.vtt	85.49kB
Lecture 21 - How to collaborate on software.mp4	230.08MB
Lecture 21 - How to collaborate on software.en.vtt	91.58kB
Lecture 20 - A first climate model.mp4	246.64MB
Lecture 20 - A first climate model.en.vtt	85.14kB
Lecture 19 - Why can't we predict the weather？.mp4	200.84MB
Lecture 19 - Why can't we predict the weather？.en.vtt	93.91kB
Lecture 18 - Libraries & parameterized types.mp4	222.85MB
Lecture 18 - Libraries & parameterized types.en.vtt	97.06kB
Lecture 17 - Time stepping and differential equations.mp4	194.01MB
Lecture 17 - Time stepping and differential equations.en.vtt	95.19kB
Lecture 16 - Optimization.mp4	120.68MB
Lecture 16 - Optimization.en.vtt	81.78kB
Lecture 15 - Linear models and simulations.mp4	123.72MB
Lecture 15 - Linear models and simulations.en.vtt	89.62kB
Lecture 14 - Discrete & Continuous.mp4	219.43MB
Lecture 14 - Discrete & Continuous.en.vtt	87.21kB
Lecture 13 - Random walks II.mp4	202.11MB
Lecture 13 - Random walks II.en.vtt	97.56kB
Lecture 12 - Random walks I.mp4	168.17MB
Lecture 12 - Random walks I.en.vtt	98.35kB
Lecture 11 - Random variables as types.mp4	207.41MB
Lecture 11 - Random variables as types.en.vtt	87.64kB
Lecture 10 - Modeling with stochastic simulation.mp4	156.72MB
Lecture 10 - Modeling with stochastic simulation.en.vtt	487.27kB
Lecture 9 - Sampling and random variables.mp4	179.83MB
Lecture 9 - Sampling and random variables.en.vtt	94.69kB
Lecture 8 - Principal Component Analysis.mp4	197.45MB
Lecture 8 - Principal Component Analysis.en.vtt	91.51kB
Lecture 7 - Structure.mp4	187.57MB
Lecture 7 - Structure.en.vtt	90.17kB
Lecture 6 - Dynamic Programming and Seam Carving.mp4	152.26MB
Lecture 6 - Dynamic Programming and Seam Carving.en.vtt	105.24kB
Lecture 5 - Inverses and Newton method.mp4	213.25MB
Lecture 5 - Inverses and Newton method.en.vtt	99.43kB
Lecture 4 - Transformations 2 - Composability and Linearity.mp4	224.54MB
Lecture 4 - Transformations 2 - Composability and Linearity.en.vtt	104.53kB
Lecture 3 - Transformations & AutoDiff.mp4	208.17MB
Lecture 3 - Transformations & AutoDiff.en.vtt	90.85kB
Lecture 2 - Transforming Images.mp4	241.05MB
Lecture 2 - Transforming Images.en.vtt	92.33kB
Lecture 1 - Course Welcome + Intro to Arrays & Images1.mp4	234.54MB
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Type: Course
Tags:

Bibtex:

@article{,
title= {MIT OCW 18.S191 Introduction to Computational Thinking (Fall 2022)},
journal= {},
author= {Prof. Alan Edelman and Prof. David P. Sanders and Prof. Charles Leiserson},
year= {},
url= {https://ocw.mit.edu/courses/18-s191-introduction-to-computational-thinking-fall-2022/},
abstract= {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://computationalthinking.mit.edu/Fall22/},
keywords= {},
terms= {},
license= {CC BY-NC-SA 4.0},
superseded= {}
}

MIT OCW 18.S191 Introduction to Computational Thinking (Fall 2022) Prof. Alan Edelman and Prof. David P. Sanders and Prof. Charles Leiserson

MIT OCW 18.S191 Introduction to Computational Thinking (Fall 2022)
Prof. Alan Edelman and Prof. David P. Sanders and Prof. Charles Leiserson