CS 747
Deep Learning

Time/Location: Thursday 4:30-7:10pm, Art and Design Building 2026  
Instructor: Jana Kosecka
Office hours:  2-3pm Wednesday
Contact: Office 4444 Research II, e-mail: kosecka@gmu.edu, 3-1876
Course web page: http://www.cs.gmu.edu/~kosecka/cs747/

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This course will cover an introduction to neural networks and deep learning. We will cover multi-layer neural networks, convolutional neural networks, recurrent neural networks, generative neural networks and deep reinforcement learning. We will discussed representative models and techniques for image classification, image and text generation, perception and action.The class will consist of programming assignments in Python (PyTorch), paper review/presentations and final project.

The course will comprise of lectures by the instructor, homeworks, paper review/presentations and final project.

Schedule, Homeworks, Handouts

Prerequisites:

CS 688, strong programming experience and willingness to participate in discussions
Students taking the class should be comfortable with linear algebra, calculus and probability

Recommended Textbook:

Deep Learning by Goodfellow et. link here

Grading:

Homeworks: 30%
Paper Presentations: 10%
Paper Summaries: 10%
Class Participation: 10%
Project Proposal/Presentation: 10%
Project Report: 30%
Late policy: Each student will have a 3 day late submission budget, which could be used towards late submission on the homeworks.

CS department Honor Code can be here.

Disability Statement If you have a documented learning disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with the Office of Disability Services (SUB I, Rm. 222; 993-2474; www.gmu.edu/student/drc) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs