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: STAT 3021, (CSci 2033 or Math 2142 or Math 4242)
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