OR 750/610. Deep Learning

Department of Systems Engineering and Operations Research
George Mason University
Fall 2019

This is a graduate level course focused on developing deep learning predictive models. We will learn both practical and theoretical aspects of deep learning. We will consider applications in engineering, finance and artificial intelligence. It is targeted towards the students who have completed an introductory courses in statistics and optimization. We will make extensive use of computational tools, such as the Python language, both for illustration in class and in homework problems. The class will consist of 9 lectures given by the instructor on several advanced topics in deep learning. At another 5 lectures students will present on a given topic.


Course staff

Course Materials: to be posted
Instructor: Vadim Sokolov (vsokolov(at)gmu.edu)
Office: Engineering Building, Room 2242
Tel: 703 993-4533
Office hours: By appointment
Lectures: Krug Hall 5. 7:20-10pm on Wed
Grades: 40% homework, 60% class presentations

List of topics


Per Topic Resources

Section 1: Architectures

Section 2: Optimization

Section 3: Theory

Section 4: Reinforcement Learning

Model-Ensemble Trust-Region Policy Optimization Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Section 5: Bayesian DL

Section 6: Practical Tricks

Other Resources

Additional Reading List



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