EE 5263. Machine Learning (Fall 2018, 2019, 2020, 2021, Graduate)

Mathematics of machine learning: vector space and linear algebra, convex optimization, gradient and stochastic gradient methods, probability theory, and basic statistical theory (hypothesis testing, maximum likelihood estimation).

Machine learning basics: concepts of training, testing, and validation, supervised learning (regression and classification), unsupervised learning, dimensionality reduction, and reinforcement learning.

Advanced topics (time-permitting): Advanced statistical inference methods and theory, high-dimensional statistics, machine learning theory.

Prerequisite: EE-3533 Probability and Stochastic Processes

Text: Lecture Notes and http://web.stanford.edu/~hastie/ElemStatLearn/