Machine Learning

Catalog description

This course covers classification, regression and clustering algorithms, as well as introductory concepts in reinforcement learning. Topics include perceptrons, logistic regression, linear regression, Naive Bayes, nearest neighbors, Support Vector Machines, and Q-learning. The description of the formal properties of the algorithms are supplemented with motivating applications in a wide range of areas including natural language processing, computer vision, bioinformatics, and music analysis.