Machine Learning

Catalog description

Study of computer algorithms that automatically acquire new knowledge and improve their own performance through experience. Theory and practical implementation of algorithms for machine learning. Topics include linear and logistic regression, artificial neural networks, Bayesian networks and learning, decision trees, kernel / support-vector machines, statistical learning methods, unsupervised learning, reinforcement learning, and other modern, emerging algorithms. Short programming assignments and/or projects with hands-on experiments with various learning algorithms will be given. Prerequisites: CSCI 515 or Instructor Approval.