Credit units: 3
Offered: Either Term 1 or Term 2
Weekly hours: 3 Lecture hours
College: Arts and Science
Department: Computer Science
A survey of Machine Learning techniques, their underlying theory, and their application to realistic data. Machine learning techniques may include Neural Networks, Support Vector Machines, Bayesian networks, Hidden Markov Models, Particle Filtering; Expectation-Maximization; Sampling; Evaluation methodologies; Over-fitting and Regularization. Software tools will be introduced for practical application.
Prerequisite(s): CMPT 317, STAT 245, and MATH 164
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