Intro to Machine Learning

Catalog description

This course provides a broad introduction to machine learning and its applications. Major topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines, neural networks); computational learning theory (bias/variance tradeoffs, VC theory, large margins); unsupervised learning; and semi-supervised learning. The course gives students both the basic ideas and intuition behind different techniques as well as a more formal understanding of how and why they work. The course discusses recent applications of machine learning, including data mining, computer vision, natural language processing, bioinformatics, and information retrieval. Throughout the course, students learn state-of-the-art machine learning models and read papers on recent developments in AI. To ensure students develop genuine understanding of how these models work rather than relying on tools that obscure the underlying mechanics, this course prohibits the use of AI except on a class project where students may choose to design new AI models or use AI to identify and solve problems of their choice. Prerequisites: CS 375 and either MATH 327 or MATH 448; or equivalents. Typically offered every semester; at least once every academic year.