MATH 455 - An Introduction to Statistical Machine Learning

3 Credit(s) | Lecture | Graded or pass/fail
Course can be counted for credit once

Description:
This course will provide an introduction to methods in statistical machine learning that are commonly used to extract important patterns and information from data. Topics include: supervised and unsupervised learning algorithms such as generalized linear models for regression and classification, support vector machines, random forests, k-means clustering, principal component analysis, and the basics of neural networks. Model selection, cross-validation, regularization, and statistical model assessment will also be discussed. The topics and their applications will be illustrated using the statistical programming language R in a practical, example/project oriented manner.

Enrollment Requirements:
Prerequisites: MATH 260 and MATH 345 and CS 110

or permission of instructor

041972:1