For full course information, visit https://github.com/mitmath/18S191
Course website: https://computationalthinking.mit.edu/Fall20/
Contents
00:00 Introduction
00:10 Matrices
01:53 Vectors in Machine Learning or Linear Algebra
08:28 Diagonal Matrices
10:08 Sparse Matrices
14:14 Another way of compressing Information-Store the Summary
16:46 Another kind of image compression-Outer Product (Multiplication tables), factorizing the Multiplication tables
23:54 Singular value decomposition (SVD)
25:18 How to use SVD for Computational Thinking?
28:22 Extracting the structure of an image using SVD
32:28 Final note
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