Statistics and Machine Learning

Computers have made it possible to collect vast amounts of data from a wide variety of sources. It is not always clear, however, how to use the data, and how to extract useful information from them. This problem is faced in a tremendous range of social, economic and scientific applications. The focus will be on some of the most useful approaches to the problem of analyzing large complex data sets, exploring both theoretical foundations and practical applications. Students will gain experience analyzing several types of data, including text, images, and biological data. Two 90-minute lectures. Prereq: MAT 202 and COS 126 or equivalent.

This course provides a comprehensive and practical background for students interested in continuous mathematics for computer science. The goal is to prepare students for higher-level subjects in artificial intelligence, machine learning, computer vision, natural language processing, graphics, and other topics that require numerical computation. This course is intended students who wish to pursue these more advanced topics, but who have not taken (or do not feel comfortable) with university-level multivariable calculus (e.g., MAT 201/203) and probability (e.g., ORF 245 or ORF 309).

A study of the essential tools for analyzing the vast amount of data that have become available in modern scientific research. Mathematical foundations of the field will be studied, along with the methods underlying the current state of the art. Probabilisitc graphical models and a unifying formalism for descrtibing and extending previous methods from statistics and engineering will be considered. Prerequisites COS402 or COS424. Undergraduates by permission only.