OR 750/610. Deep Learning

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

Course Material

Instructor: Vadim Sokolov (vsokolov(at)gmu.edu)
Office hours: By appointment
TA: Wanru Li (wli15(at)masonlive.gmu.edu)
Office Hours: Mon 4-6 pm at ENGR 2216

Presentation Materials

All of the posts based on the presentations are posted here dlclass2019.github.io


Please upload your materials to here. To post your blog, send me your GiHub handle and use this instructions to add your post


Please upload your 1-page proposals to here. Deadline is 11/6. Proposal has to have names and emails of the team members. Description of data set, problem to be solved and proposed architectures.




Research paper presentations by 750 students

During weeks 9-12, this class will be run in a seminar mode. A team of students will prepare a topic and will lead the discussion and another team will write a blog-post about the class and will post it on Medium. Students responsible for posting the blog summary will be different from the ones charged with leading the topic discussion, but should work closely with the leaders on the posted write-up.

Leading Team. The team responsible for leading a class should:

Blogging Team. The team responsible for blogging a class should:

Suggested Research Paper Topics

Data analysis projects by 610 students

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 9 of the class you should have a team formed and data set + analysis problem identified. You need to email me a 0.5-1 page description of the data and problem you are trying to solve for my feedback and approval. During week 13, you will have a time slot to present your findings. You are also encouraged (although it is not required) to post results of your analysis on Medium, if you think it is worth sharing.

Course staff

Lectures: Exploratory Hall L111. 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



Other courses with good web presence


Misc Links

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