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Dec 22, 2024
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CSCI 6100 - Machine and Computational Learning
Introduction to the theory, algorithms, and applications of automated learning (supervised, reinforcement, and unsupervised), how much information and computation are needed to learn a task, and how to accomplish it. Emphasis will be given to unifying approaches coming from statistics, function approximation, optimization, and pattern recognition. Topics include: Decision Trees, Neural Networks, RBF’s, Bayesian Learning, PAC Learning, Support Vector Machines, Gaussian processes, Hidden Markov Models.
Prerequisites/Corequisites: Prerequisites: familiarity with probability, linear algebra, and calculus.
When Offered: Offered on availability of instructor.
Credit Hours: 3
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