MATH-642 Introduction to Statistical Learning
Spring for 2017-2018
Math 642 Introduction to Statistical Learning. Machine or statistical learning is concerned with algorithms that automatically improve their performance through experience and active feedback. Examples are programs that learn to recognize human faces, recommend music and movies, and drive autonomous vehicles. This course covers theory and practical algorithms for machine learning from a number of perspectives. We cover topics such as neural networks, Bayesian belief networks, statistical learning methods, unsupervised learning and reinforcement learning. Homework assignments will include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. This course is designed to provide a solid background in the methodologies, mathematics and algorithms associated with automatic learning.

Text: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.

Must be enrolled in one of the following Levels:
MN or MC Graduate
Must be enrolled in one of the following Majors:
Mathematics and Statistics

Credits: 3
Prerequisites: Math 501 and a course in statistics, either Matlab or R, with R strongly preferable.
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