COMP 640: Graduate Seminar in Machine Learning


We will pick popular papers in Deep Learning/Machine Learning and discuss them.

This year our theme is The Unification of Deep Learning Systems. We start with Understanding Recommendation Systems and why that is the largest workload. NLP is looking more like recommendation systems and growing. Vision is going towards NLP starting last year.

The class will start with an informal short introduction (20 minutes) to the key concepts and contributions. Then we will open the floor to two pre-divided groups. One of them will argue why this paper deserves all the credit and what subtleties of Deep Learning is the paper bringing to the table. The other group will serve as a devil's advocate. Their job is to argue why, based on what was known till the publication of the paper, there is nothing new presented in the paper OR argue why the claims are questionable based on other more recently published works.

An argument is only valid if it is supported by at least one of the following:

- Mathematical Reductions and Equivalence.

- Claims/Arguments/Theorems from published works.

- Experimental data and plots that appeared in published works.

The class aims to introduce graduate students to a rigorous process of forming valid scientific arguments to judge an idea. The hope is that this debate process will serve four purposes:

1. Create a practice of questioning every claim, even made in some of the most famous papers

2. To get a deeper understanding of the paper.

3. Understand what community considers a contribution that will help them formulate their thought process before writing papers,

4. Make them better future reviewers.

The class will be divided randomly into FOR and AGAINST group. Papers will be posted one week in advance. Both the group will jointly submit a 2-page report of their findings and arguments.

Grading and Logistics

For 1 credit: Class and 1-page report participation, sign up for one informal short introduction, and sign up for one discussion summarization. In addition students can undergo a semester long research project for 3 credits. For 3 credits requirements discuss with the instructor.


A rigorous course in machine learning is required. We will be discussing advanced papers in ML papers every week.

Presentations and Summarization Logistics

Participation in debate along with the 2-page report. In addition, each student should sign up for 1 class to present (2 students per class) and 1 class to summarize the discussions (2 students per class). You cannot summarize the same class that you presented. The summarization should be submitted no later than a week of the presentation.

Please sign-up for scribe and presentation assignment at Google Spreadsheet


Students with Disability

If you have a documented disability that may affect academic performance, you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / / x5841) to determine the accommodations you need; and 2) meet with me to discuss your accommodation needs.