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(Dual-listed with COM S 574). (3-1) Cr. 3.

Prereq: COM S 311; STAT 330 or STAT 305 or STAT 341 or STAT 347; MATH 165; ENGL 250; for graduate credit: graduate standing or permission of instructor
Introduction to concepts, tools and techniques of machine learning for applications. Selected machine learning algorithms in practical data mining tasks such as classification, regression, and clustering, e.g., association rules, decision trees, linear models, Bayesian learning, support vector machines, artificial neural networks, instance-based learning, probabilistic graphical models, ensemble learning, and clustering algorithms. Selected applications in data mining and pattern recognition.