Machine Learning

Catalog description

An introduction to machine learning for students with some mathematical maturity. Topics include: machine learning in relation to artificial intelligence, data sources and characteristics, linear and non-linear regression, machine learning concepts like the bias-variance tradeoff, linear and non-linear classification, hidden Markov models and the expectation-maximization algorithm, unsupervised learning, and deep learning. Examples will be drawn from several domains including natural language processing.