IFT 6132 - Winter 2021: Class projects


The project gives you the opportunity to study in greater depth some concepts of the course, to put them in practice or to pursue some related research direction. The topic has to be linked with algorithms, concepts or methods presented in class, but beyond this requirement, the choice is quite open. There is considerable flexibility in your choice of project, and it should be tailored to your interests.

The project should be grounded on at least one research paper (which you can propose yourself). The project can be done invidually or in teams of two to three people. Once your group and project topic is chosen, you submit a plan and description on Studium so that I can validate it and potentially give you some feedback (by Tuesday April 7th).

There are many possible types of project for this class, and in particular one can decide to focus more on structured prediction or on optimization aspects.

A standard class project would contain the following 3 components:

  1. An article review around a given topic. It can be chosen from the references given during the lectures, or in the list below, or some other article of your choice. This means to read and understand a specific research article.

  2. An implementation of the method.

  3. An experimentation with real data. This means to apply the method on real data and report your findings and observations. If the paper is quite dense and theoretical, then an experimentation on simulated / synthetic data is sufficient.

Here are examples of types of projects:


The final class project counts for 80%. Evaluation will be made on:

  1. A report (of about 4 to 8 pages) presenting the project and the obtained results (for applicative projects), to be given by April 30th, 2020 on Studium. The report has to be written in such a way that any student who has followed the class can understand the gist. The report has to clearly present (in French or English) the studied problem and the existing approaches. You will be more evaluated on the clarity of the report rather than on its length. To train you to write professional research papers, you should use LaTeX in a modified ICML 2018 template format (download the template here). You may use appendices for additional details beyond 8 pages if you want, but be aware that as in standard conference reviewing, I might only read the first 8 pages (so the main content has to be there), and also, succinctness is more valued here than length!

  2. A poster presentation of 6-10 minutes (length will be decided later once I know then number of teams), from A0 landscape poster pdf, which will be displayed in Gather.town during a poster session on *Thursday April 29th 1:30pm to 3:30pm*. (More info about the virtual poster session logistics and how to use Gather.town to come) The presentation (in French or English) is also geared towards other students and the goal is to highlight in the allocated time the salient points of your project. Like in a regular conference poster session, students are encouraged to attend other student posters. Other guidelines for the poster and presentation:

    1. The poster should be in English so that all students can understand it (but your presentation to me can be in French if you prefer).

    2. The content of your poster has a double purpose:

      1. to explain clearly to other students of the class the model, problems and algorithms you have worked on, and any interesting observations that you have made.

      2. to be the support of your short oral presentation of your project

    3. The (6?) minutes timing will be strict as we want to be able to ask you a couple of questions in addition and there are many of you. We highly recommend that you prepare ahead of time what you will say during these minutes. Highlight your understanding and the main things you have done (model, main algorithmic ideas, data, results).

    4. Make sure that you also include on your poster:

      1. your group number

      2. which paper the project was based on

      3. mention how the implementation was done (did you just download their code and used it; did you re-implement everything yourself in language Y; etc.)


The various steps are summarized below.

Before 4/9 Chooose your group and your project and give a description on Studium.
On 4/29 Poster session on your project during the 1:30–3:30pm time slot
Before 4/30 Submit your project report (4-8 pages, ICML format, on Studium)

Examples of projects from previous years

As an example, here were the titles for the projects from past years:

List of references and examples

Here is a list of references to give you ideas. It is definitively not exhaustive – feel free to Google a bit! Also, a lot of these have code and datasets online, so make sure to look for these. Also consider the other references that I have given in class.


With deep learning:

Task losses:

Approximate inference:




Incremental gradient methods and friends

Saddle point methods


Computer vision