CS/ECE 861 Theoretical Foundations of Machine Learning
Description Advanced mathematical theory and methods of machine learning. Statistical learning theory, Vapnik-Chevronenkis Theory, model selection, high-dimensional models, nonparametric methods, probabilistic analysis, optimization, learning paradigms. Prereq CS/ECE 761 or ECE 830 (While not required, CS/ECE 532 Matrix Methods in Machine Learning, Math 521 Analysis I, ECE 730 Modern Probability Theory and Stochastc Processes, CS/ECE 524 Introduction to Optimization, and similar math courses are helpful) Instructor Professor Jerry Zhu, email@example.com Homeworks Assignments are posted in the Canvas system. Course discussion forum Piazza discussions Time and location Lectures in CS 1325, MWF 9:30-10:45am, see calendar below Office hour Wednesdays 2-3pm CS 6391 Exam Midterm exam: Wed Feb 27 9:30-10:45am in-class Final exam: Around mid-April, in-class All exams are closed book. Bring copious amount of blank scratch paper. One 8.5x11 sheet of paper with notes on both sides allowed (handwritten or typed). Lectures and readings on the syllabus page are required. You are responsible for topics covered in lecture. You should have knowledge sufficient to work through simple examples. Exam grading questions must be raised with the instructor within one week after it is returned. Project An open machine learning project, done individually or in groups of two. Requires an analysis component. Proposal due Apr 5 before class. Report due end-of-day May 3, 4-8 pages. Syllabus (tentative, subject to change) Statistical Learning Empirical risk minimization, PAC learning [SS 2, 3, 4] concentration inequalities [SS B.1--B.5] Structural risk minimization and minimum description length [SS 7] Bias-complexity tradeoffs [SS 5] Vapnik-Chevronenkis dimension [SS 6] Rademacher complexities [SS 26, ZRG'09] Online learning batch perceptron [SS 9] Halving and Littlestone dimension [SS 21.1, 21.2] Online convex optimization [SS 21.3, 21.4] Advanced Learning Paradigms Active learning [H 1, 2, 5.1, ICML09 tutorial] Bandit algorithms [LS 6, 7; BC 1, 2, 3] Machine Teaching [GK, KSZ] References [BC] S. Bubeck and N. Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. In Foundations and Trends in Machine Learning, Vol 5: No 1, 1-122, 2012. [H] Steve Hanneke. Theory of Active Learning [LS] Tor Lattimore and Csaba Szepesvari. Bandit Algorithms. [SS] Understanding Machine Learning: From Theory to Algorithms Shai Shalev-Shwartz and Shai Ben-David , Cambridge University Press 2014 Grading: Homeworks (30%), exam (40%), project (30%). Class learning outcome Student will be able to: - derive sample complexity bounds using concentration of measure inequalities - analyze bias-variance tradeoffs and model selection criteria - derive rates of convergence for nonparametric machine learning algorithms - gain familiarity with various machine learning paradigms, including supervised, unsupervised, active, multitask, and online learning.