Explore matrices, vectors, and advanced mathematical concepts in this 33-minute lecture from MIT's 18.S191 Fall 2020 course. Dive into the applications of matrices in machine learning and linear algebra, understand diagonal and sparse matrices, and discover innovative image compression techniques. Learn about singular value decomposition (SVD) and its role in computational thinking. Examine how to extract image structure using SVD, and gain insights into advanced mathematical concepts applied to real-world problems. Engage with the Julia programming community by contributing timestamps or captions to enhance the video's accessibility and discoverability.
Overview
Syllabus
Introduction.
Matrices.
Vectors in Machine Learning or Linear Algebra.
Diagonal Matrices.
Sparse Matrices.
Another way of compressing Information-Store the Summary.
Another kind of image compression-Outer Product(Multiplication tables), factorizing the Multiplication tables.
Singular value decomposition(SVD).
How to use SVD for Computational Thinking?.
Extracting the structure of an image using SVD.
Final note.
Welcome!.
Help us add time stamps or captions to this video! See the description for details..
Taught by
The Julia Programming Language
