OR 750. Deep Learning

Department of Systems Engineering and Operations Research
George Mason University
Fall 2018

This is a PhD 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 12 lectures given by the instructor on several advanced topics in deep learning. At another 3 lectures students will present on the topic of their choice.


Paper Presentations


Course staff

Course Materials: Dropbox folder
Instructor: Vadim Sokolov (vsokolov(at)gmu.edu)
Office: Engineering Building, Room 2242
Tel: 703 993-4533
Office hours: By appointment
Lectures: Nguyen Engineering Building 2241 (Fusion Lab). 4:30-7:10pm on Mondays
Grades: 40% homework (individual), 60% final research project (group)

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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