[Coursera] Coding the Matrix: Linear Algebra through Computer Science Applications
Philip Klein (Brown University)



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coursera-coding-the-matrix (73 files)
assignments/python_lab1.pdf460.87kB
assignments/python_lab2.pdf127.41kB
assignments/python_lab3.pdf136.48kB
assignments/python_lab4.pdf142.70kB
assignments/python_lab5.pdf198.59kB
assignments/python_lab6.pdf165.62kB
assignments/python_lab7.pdf166.02kB
assignments/python_lab8.pdf4.82MB
assignments/python_lab9.pdf193.57kB
assignments/README.txt0.27kB
lectures/tuts/Coding the Matrix Linear Algebra through Computer Science Applications 8.0 How to submit assignments.mp49.20MB
lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.0 Course Introduction Part 1 (953).mp4130.04MB
lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.1 Course Introduction Part 2 (849).mp490.84MB
lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.2 The Function The function and other preliminaries (2055).mp4148.30MB
lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.3 The Field Introduction to complex numbers (552).mp436.54MB
lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.4 The Field Playing with C (1519).mp492.15MB
lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.5 The Field Playing with GF(2) (1028).mp471.38MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.0 The Vector What is a vector (820).mp451.36MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.1 The Vector Vector addition and scalar-vector multiplication (1016).mp463.58MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.2 The Vector Dictionary-based representations of vectors (910).mp457.62MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.3 The Vector Vectors over GF(2) (918).mp456.72MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.4 The Vector Dot-product (849).mp454.09MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.5 The Vector Dot-product of vectors over GF(2) (444).mp413.55MB
lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.6 The Vector Solving a triangular system of linear equations (400).mp410.02MB
lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.0 The Vector Space Linear combinations.mp429.36MB
lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.1 The Vector Space Span.mp422.53MB
lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.2 The Vector Space Geometry of Sets of Vectors.mp480.21MB
lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.3 The Vector Space Vector spaces.mp432.25MB
lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.4 The Vector Space Checksum function.mp410.35MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.0 The Matrix What is a matrix.mp485.38MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.1 The Matrix Matrix-vector and vector-matrix multiplication.mp433.58MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.2 The Matrix Matrix-vector multiplication in terms of dot-products.mp432.95MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.3 The Matrix Null space.mp411.17MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.4 The Matrix Error-correcting codes.mp414.43MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.5 The Matrix Matrices and their functions.mp459.52MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.6 The Matrix Linear functions.mp489.87MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.7 The Matrix Matrix-matrix multiplication.mp470.58MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.8 The Matrix Matrix-matrix multiplication and function composition.mp419.35MB
lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.9 The Matrix Matrix inverse.mp477.70MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.0 The Basis Coordinate systems.mp410.91MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.1 The Basis Lossy compression.mp411.46MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.10 The Basis The Exchange Lemma.mp428.07MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.2 The Basis Algorithms for finding a set of generators.mp412.90MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.3 The Basis Minimum spanning forest.mp475.45MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.4 The Basis Linear dependence.mp4107.27MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.5 The Basis Basis.mp424.20MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.6 The Basis Unique representation.mp47.18MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.7 The Basis Change of basis.mp414.49MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.8 The Basis Perspective rendering.mp432.91MB
lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.9 The Basis Perspective rectification.mp466.15MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.0 Dimension The size of a basis.mp436.20MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.1 Dimension Dimension and rank I.mp418.66MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.2 Dimension Dimension and rank II.mp4109.94MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.3 Dimension Direct sum.mp430.73MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.4 Dimension Dimension and linear functions I.mp446.62MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.5 Dimension Dimension and linear functions II.mp429.88MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.6 Dimension Two representations of vector spaces.mp439.60MB
lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.7 Dimension Threshold secret sharing.mp424.70MB
lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.0 Gaussian Elimination Echelon form.mp416.11MB
lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.1 Gaussian Elimination Transforming a matrix to echelon form.mp437.83MB
lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.2 Gaussian Elimination Using Gaussian elimination to solve a system of equations.mp435.68MB
lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.3 Gaussian Elimination Factoring integers.mp4113.64MB
lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.4 The Inner Product The inner product.mp418.61MB
lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.5 The Inner Product Orthogonality.mp4106.68MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.0 Orthogonalization Finding the closest point in a plane.mp415.47MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.1 Orthogonalization Projection orthogonal to multiple vectors.mp4122.65MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.2 Orthogonalization Building an orthogonal set of generators.mp441.60MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.3 Orthogonalization Computing a basis.mp427.63MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.4 Orthogonalization Orthogonal complement.mp437.25MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.5 Orthogonalization Two ways to find a basis for the null space.mp420.58MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.6 Orthogonalization The QR factorization.mp420.44MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.7 Orthogonalization Using the QR factorization to solve a matrix equation Ax = b.mp441.83MB
lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.8 Orthogonalization Applications of least squares.mp434.02MB
Type: Course
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Bibtex:
@article{,
title= {[Coursera] Coding the Matrix: Linear Algebra through Computer Science Applications},
keywords= {},
journal= {},
author= {Philip Klein (Brown University)},
year= {2015},
url= {},
license= {},
abstract= {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).},
superseded= {},
terms= {}
}