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The goals of this course are to introduce basic theory of deep learning in data science applications, to understand how deep learning algorithms work at a high level, and to apply deep learning algorithms to key data science problems in different disciplines. The course introduces all relevant topics in deep learning: neural networks, backpropagation, convolution neural networks, recurrent neural networks and deep reinforcement learning. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various deep learning methods.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: INFO 213 [Min Grade: D] and MATH 201 [Min Grade: D]