Machine Learning

4.0 credits

Mathematical foundations of classification, regression, and decision making. Supervised algorithms covered include perceptrons, logistic regression, support vector machines, and neural networks. Directed and undirected graphical models. Numerical parameter optimization, including gradient descent, expectation maximization, and other methods. Introduction to reinforcement learning. Proofs covered as appropriate. Significant programming projects will be assigned. This course involves a lot of math and algorithms. You should know multivariable calculus, linear algebra, and some algorithms.