rohinarora/EECE5644-Machine_Learning
Graduate course on Machine Learning
This is a collection of lecture notes and materials for a graduate-level machine learning course. It covers foundational topics like linear algebra, probability, and optimization, then progresses into core machine learning concepts such as PCA, Bayesian decision theory, and Expectation-Maximization. The intended user is a graduate student or researcher looking to learn or review advanced machine learning principles.
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Use this if you are a graduate student or researcher seeking comprehensive lecture materials to understand fundamental and advanced machine learning algorithms and theories.
Not ideal if you are looking for an introductory, beginner-friendly guide to machine learning concepts or a practical, code-focused implementation tutorial.
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Nov 28, 2019
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