OR 610. Deep Learning

Department of Systems Engineering and Operations Research
George Mason University
Spring 2022


Course Material

Instructor: Vadim Sokolov (vsokolov(at)gmu.edu)
Location and time: Aquia, room 347; 7:20-10pm Mondays
Office hours: By appointment

List of topics and tentative

Data analysis projects

You will work in a team of up to 3 people on a Kaggle-like project and will apply deep learning to solve a prediction or data generation problem. By week 8 of the class you should have a team formed and data set + analysis problem identified. You need to submit a 0.5-1 page description of the data and problem you are trying to solve for my feedback and approval. Proposal has to have names and emails of the team members. Description of data set, problem to be solved and proposed architectures. You will post results of your analysis on the class blog post. The final project will be graded on presentation, writing and analysis.

Group Work

Both projects and homework can be done in a groups of size of up to 3 people. You can change groups in between. If you do a homework in a gorup, it means that all of the members of the group do it individually and can consult with each other. You can also do 1 submission per group if you prefer. You can use “group” section of the piazza page to find teammates if you need any. If you need help finding a group, please email me.

Grading

Each hw is 10 points, project is 30.

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.

Books

Per Topic Resources

Architectures

Optimization

Theory

Reinforcement Learning

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

Bayesian DL

Practical Tricks

Other Resources

Additional Reading List

Blogs

Videos

Other courses with good web presence

Tools

Misc Links