Philip Klein (Brown University)

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coursera-coding-the-matrix (73 files)

assignments/python_lab1.pdf | 460.87kB |

assignments/python_lab2.pdf | 127.41kB |

assignments/python_lab3.pdf | 136.48kB |

assignments/python_lab4.pdf | 142.70kB |

assignments/python_lab5.pdf | 198.59kB |

assignments/python_lab6.pdf | 165.62kB |

assignments/python_lab7.pdf | 166.02kB |

assignments/python_lab8.pdf | 4.82MB |

assignments/python_lab9.pdf | 193.57kB |

assignments/README.txt | 0.27kB |

lectures/tuts/Coding the Matrix Linear Algebra through Computer Science Applications 8.0 How to submit assignments.mp4 | 9.20MB |

lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.0 Course Introduction Part 1 (953).mp4 | 130.04MB |

lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.1 Course Introduction Part 2 (849).mp4 | 90.84MB |

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).mp4 | 148.30MB |

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).mp4 | 36.54MB |

lectures/week0-the-function-and-the-field/Coding the Matrix Linear Algebra through Computer Science Applications 0.4 The Field Playing with C (1519).mp4 | 92.15MB |

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).mp4 | 71.38MB |

lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.0 The Vector What is a vector (820).mp4 | 51.36MB |

lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.1 The Vector Vector addition and scalar-vector multiplication (1016).mp4 | 63.58MB |

lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.2 The Vector Dictionary-based representations of vectors (910).mp4 | 57.62MB |

lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.3 The Vector Vectors over GF(2) (918).mp4 | 56.72MB |

lectures/week1-the-vector/Coding the Matrix Linear Algebra through Computer Science Applications 1.4 The Vector Dot-product (849).mp4 | 54.09MB |

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).mp4 | 13.55MB |

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).mp4 | 10.02MB |

lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.0 The Vector Space Linear combinations.mp4 | 29.36MB |

lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.1 The Vector Space Span.mp4 | 22.53MB |

lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.2 The Vector Space Geometry of Sets of Vectors.mp4 | 80.21MB |

lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.3 The Vector Space Vector spaces.mp4 | 32.25MB |

lectures/week2-the-vector-space/Coding the Matrix Linear Algebra through Computer Science Applications 2.4 The Vector Space Checksum function.mp4 | 10.35MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.0 The Matrix What is a matrix.mp4 | 85.38MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.1 The Matrix Matrix-vector and vector-matrix multiplication.mp4 | 33.58MB |

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.mp4 | 32.95MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.3 The Matrix Null space.mp4 | 11.17MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.4 The Matrix Error-correcting codes.mp4 | 14.43MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.5 The Matrix Matrices and their functions.mp4 | 59.52MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.6 The Matrix Linear functions.mp4 | 89.87MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.7 The Matrix Matrix-matrix multiplication.mp4 | 70.58MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.8 The Matrix Matrix-matrix multiplication and function composition.mp4 | 19.35MB |

lectures/week3-the-matrix/Coding the Matrix Linear Algebra through Computer Science Applications 3.9 The Matrix Matrix inverse.mp4 | 77.70MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.0 The Basis Coordinate systems.mp4 | 10.91MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.1 The Basis Lossy compression.mp4 | 11.46MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.10 The Basis The Exchange Lemma.mp4 | 28.07MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.2 The Basis Algorithms for finding a set of generators.mp4 | 12.90MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.3 The Basis Minimum spanning forest.mp4 | 75.45MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.4 The Basis Linear dependence.mp4 | 107.27MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.5 The Basis Basis.mp4 | 24.20MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.6 The Basis Unique representation.mp4 | 7.18MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.7 The Basis Change of basis.mp4 | 14.49MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.8 The Basis Perspective rendering.mp4 | 32.91MB |

lectures/week4-the-basis/Coding the Matrix Linear Algebra through Computer Science Applications 4.9 The Basis Perspective rectification.mp4 | 66.15MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.0 Dimension The size of a basis.mp4 | 36.20MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.1 Dimension Dimension and rank I.mp4 | 18.66MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.2 Dimension Dimension and rank II.mp4 | 109.94MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.3 Dimension Direct sum.mp4 | 30.73MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.4 Dimension Dimension and linear functions I.mp4 | 46.62MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.5 Dimension Dimension and linear functions II.mp4 | 29.88MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.6 Dimension Two representations of vector spaces.mp4 | 39.60MB |

lectures/week5-dimension/Coding the Matrix Linear Algebra through Computer Science Applications 5.7 Dimension Threshold secret sharing.mp4 | 24.70MB |

lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.0 Gaussian Elimination Echelon form.mp4 | 16.11MB |

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.mp4 | 37.83MB |

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.mp4 | 35.68MB |

lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.3 Gaussian Elimination Factoring integers.mp4 | 113.64MB |

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.mp4 | 18.61MB |

lectures/week6-gaussian-elimination-and-the-inner-product/Coding the Matrix Linear Algebra through Computer Science Applications 6.5 The Inner Product Orthogonality.mp4 | 106.68MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.0 Orthogonalization Finding the closest point in a plane.mp4 | 15.47MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.1 Orthogonalization Projection orthogonal to multiple vectors.mp4 | 122.65MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.2 Orthogonalization Building an orthogonal set of generators.mp4 | 41.60MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.3 Orthogonalization Computing a basis.mp4 | 27.63MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.4 Orthogonalization Orthogonal complement.mp4 | 37.25MB |

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.mp4 | 20.58MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.6 Orthogonalization The QR factorization.mp4 | 20.44MB |

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.mp4 | 41.83MB |

lectures/week7-orthogonalization/Coding the Matrix Linear Algebra through Computer Science Applications 7.8 Orthogonalization Applications of least squares.mp4 | 34.02MB |

@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= {} }