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Introduces a machine learning technique called deep learning and its applications, as well as core machine learning concepts such as data set, evaluation, overfitting, regularization and more. Covers neural network building blocks: linear and logistic regression, followed by shallow artificial neural networks and a variety of deep networks algorithms and their derivations. Includes implementation of algorithms and usage of existing machine learning libraries. Explores the usage of deep learning on a variety of problems including image classification, speech recognition, and natural language processing. Concludes with student-chosen project demonstrations accompanied by a conference-style paper.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: CS 520 [Min Grade: C] and CS 570 [Min Grade: C] and CS 571 [Min Grade: C]