Stony Brook University 3 credits ESE
The course presents the fundamental methods used in Machine Learning for engineering applications. The course discusses representation models for learning, extraction of frequent patterns, classification, clustering, and application of these techniques for different engineering applications. Supervised and unsupervised learning methods are discussed. The course includes two projects that involve devising and implementing the studied techniques and their evaluation using standard benchmark data. 3 credits Prerequisite: ESE 224
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