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/

EE-3533: Probability and Stochastic Processes (Spring 2019, 2020, 2021, Undergraduate)

Introduction to set theory. Sample space and probability axioms. Conditional probability: Bayes theorem and total probability theorem. Independence. Discrete random variables: probability mass function (pmf), expectation, functions of a random variable, conditional pmf, independent random variables. Continuous Random Variables: cumulative distribution function, probability density function, convolution. Three important theorems in probability: law of large numbers, central limit theorem, and Markov inequality.

Text: Lecture Notes and ECE 313 Notes by Prof. Bruce Hajek (UIUC).