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MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
About this film
Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning. Note: Videos of Lectures 28 and 29 are not available because those were in-class lab sessions that were not recorded. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
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MIT OpenCourseWare
Subjects
18.065, 18.0651, data analysis, signal processing, image processing, machine learning, linear algebra, computation, singular value decomposition, least squares, weighted least squares, covariance matrices, correlation matrices, directed graphs, undirected graphs, matrix factorizations, neural nets, Signal Processing, Applied Mathematics, Computation, Linear Algebra
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