Introduction to Machine Learning

Undergraduate catalogue

This course is an introduction to various machine learning paradigms and algorithms, providing a foundation for further study or independent work in machine learning and data science. Topics include linear models for classification and regression, support vector machines, clustering, dimensionality reduction, deep neural networks, Markov decision processes, planning, and reinforcement learning. The course goals are threefold: to understand the landscape of machine learning, to compute the math behind techniques, and to use Python and relevant libraries to implement and apply various methods. Prerequisite: MAT 103, MAT 202, and COS 226.