Course: STAT 37711=CAAM 37711, DATA 37711
Title: Foundations of Machine Learning and AI - Part I
Instructor: Victor Veitch
Class Schedule: Sec 1: TR 3:30 PM-4:50 PM in Stuart 102
Description: This course is an introduction to machine learning targeted at students who want a deep understanding of the subject. Topics include modern approaches to supervised learning, unsupervised learning, and the use of machine learning in estimating real-world effects. In principle, no previous exposure to machine learning is required. However, students are expected to have mathematical maturity at the level of an advanced undergraduate, including being comfortable with linear algebra, multivariate calculus, and (non-measure theoretic) statistics and probability. Assignments include programming in python (and pytorch).