Prerequisites: EK381 or equivalent; EK102 or equivalent; MA225 or equivalent; EK125 or equivalent. This is an introductory graduate course in (classical) machine learning covering the basic principles and methods of four major non-sequential supervised and unsupervised learning problems namely, classification, regression, clustering, and dimensionality reduction. A variety of contemporary applications will be explored through homeworks and a project.

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