CSCI5521

Problem formulations and concepts of generalization, overfitting/under-fitting, and model selection. Statistical foundations: maximum likelihood, maximum a posteriori, Expectation Maximization (EM), Bayesian estimation, and non parametric methods. Mathematical foundations and optimization: gradient descent, back propagation, regularization, quadratic programming. Perceptrons, neural networks, and deep learning methods. Kernel methods and Support Vector Machines (SVMs). Machine Learning ethics.

prereq: Update: (CSCI 2033 OR MATH 4242) AND (STAT 3021 OR EE 3025 OR IE 3521), OR Graduate Student Standing

013404

Every Fall & Spring