Units: 4 Overview of machine learning, regression and classification, clustering and dimensionality reduction, loss-functions, over/under-fitting, cross-validation, kernel machines, decision trees and neural networks. Prerequisite: (EE 155 or BME 210 or CE 108 or CHE 305 or CSCI 103 or ISE 150 or ITP 115 or ITP 165 or ITP 168) and (EE 141 or MATH 225 or MATH 235) Recommended Preparation: Linear systems on the level of EE 301, probability and/or statistics on the level of EE 364 or MATH 407 or CE 119 or ISE 220 or ISE 225 Duplicates Credit in CSCI 467 Instruction Mode: Lecture, Discussion Grading Option: Letter
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