CMSC 636: Neural Nets and Deep Learning

The course will assume undergraduate-level background in programming, algorithms, linear algebra, calculus, statistics and probability. Topics ranging from fundamental learning rules, functional, cascade correlational, recurrent and gradient descent networks, to neocognitron, softmax, deep convolutional networks, autoencoders and pretrained deep learning (restricted Boltzmann machines). Students will be required to work in teams on a class paper. 

Total Credit Hours: 3
Spring 2019
Enrollment restricted to students with graduate standing in computer science.
Milos Manic
Instructor Email:

Weekly Live Sessions

Mon Tue Wed Thu Fri Sat
3:30 - 4:45 PM 3:30 - 4:45 PM

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Questions? Contact the VCU CGEP Director:

Dr. Gregory Triplett   •   Ph: (804) 828-5387   •