This Ph.D. level course will provide an overview of machine learning and its algorithmic paradigms, and explore recent topics on learning, inference, and decision-making in the presence of large data sets. Emphasis will be made on theoretical insights and algorithmic principles.
This is a Ph.D.-level course for students with strong quantitative and mathematical backgrounds. Basic graduate-level probability and statistics classes as prerequisites are recommended. Students should be comfortable with probability theory, statistics, numerical linear algebra, and basic knowledge of continuous optimization. MBA students require instructor permission: strict.
Description and/or course criteria last updated: October 27 2023
