CSCI-5622 (3) Machine Learning

Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended. Requisites: Requires prerequisite courses of CSCI 2400 and CSCI 3104 (all minimum grade C). Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.