IFT 3710/6759 - Projets (avancés) en apprentissage automatique

Course description

The objective of this course is to prepare you, the students, for tackling real-world machine learning projects. During the course, you will work on the main stages of machine learning projects, including data acquisition, data pre-processing, model training, analysis of results and presentation of results and conclsions. Skills developed during this course include literature review of a particular problem, practical and theoretical machine learning, Python for data science, PyTorch, version control with git and basic Linux commands.

Course outline

  1. Introduction to the course
  2. Version control with git and GitHub
  3. Python basics and Linux commands
  4. HPC computing
  5. Review of machine learning
  6. Review of deep learning
  7. Advanced PyTorch tutorial
  8. Deep learning tricks and Q&A
  9. Projects work

Evaluation criteria

Students will be evaluated entirely according to their work on projects. Depending on the complexity of the projects, you will be required to take part in one or two projects and work in teams of 2–4 people. You can choose from a list of projects prepared by the instructors, or propose your own projects, provided they meet certain criteria. The final evaluation will take the following into account:

  • Difficulty of the project
  • Performance of the developed algorithms
  • Oral presentation
  • Written report
  • Quality of the code

Important note: The evaluation criteria will be slightly relaxed for undergraduate students (IFT 3710).

All students will be required to pass a basic skills test and interview before starting the project work, in order to ensure that they are ready to work on an advanced machine learning project in a team.

Useful links

Winter term 2022

Classes take place on:

  • Wednesdays, 16:30–18:30 (ET)
  • Fridays, 09:30–11:30 (ET)

Due the pandemic situation, la Faculté des arts et des sciences of the Université de Montréal has delayed the start of the term until January 10th. Therefore, the first session of this course will be on Wednesday January 12th. For the same reasons, classes will be online at least until January 31th.

Resources