CMSC 403 - Machine Learning Credits: 4

This introductory course gives an overview of machine learning. This is a wide ranging field including topics such as: classification, linear regression, Principal Component Analysis (PCA), neural networks, bagging and boosting, support vector machines, hidden Markov models, Bayesian networks, Q-learning, reinforcement learning.

Prerequisite(s): (one course from: MATH 117A, MATH 117, MATH 217, or MATH 375) and CMSC 310
Graduate Credit: This course is not available for graduate credit.