New York University 3 credits Elect. Engineering - ECE GRAD
This course presents the main concepts, techniques, algorithms, and state-of-the-art approaches in modern machine learning from both theoretical and practical perspective. Students will be exposed to new mathematical proof techniques and up-to-date machine learning coding environments and benchmark datasets. The program of the course includes empirical risk minimization, support vector machines, kernels, optimization techniques for machine learning, clustering, principal component analysis, Expectation-Maximization, online learning algorithms, boosting, decision trees, graphical models, and deep learning. The course contains tutorials on selected most popular machine learning software environments. The course finally emphasizes interesting and important open problems in the field. Mathematical maturity (https://en.wikipedia.org/wiki/Mathematical_maturity) is required from students registering for the course. | Prerequisites: A grade of A- or better in (ECE-GY 6143 or CS-GY 6923) and a grade of B+ or better in ECE-GY 6303.
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