Continual Learning: Towards “Broad” AI

IFT 6760B Winter 2022, Université de Montréal / Mila - Quebec AI Institute

Course Description

Stephen Hawking famously said, ‘Intelligence is the ability to adapt to change. While today’s AI systems can achieve impressive performance in specific tasks, from accurate image recognition to super-human performance in games such as Go and chess, they are still quite "narrow", i.e. not being able to easily adapt to a wide range of new tasks and environments, without forgetting what they have learned before - something that humans and animals seem to do naturally during their lifetime. This course will focus on the rapidly growing research area of machine learning called continual learning (CL) which aims to push modern AI from "narrow" to "broad", i.e. to develop learning models and algorithms capable of never-ending, lifelong, continual learning over a large, and potentially infinite set of different environments and tasks. In this course, we will review the state-of-the-art literature on continual learning in modern ML, and some related work on stability vs plasticity in neuroscience. We focus on the catastrophic forgetting problem and recent approaches to overcoming it in deep neural networks, including regularization, replay and dynamic architecture methods; we also consider different CL settings (e.g., task-incremental, class-incremental, task-agnostic, etc). Furthermore, we review some recent advances in out-of-distribution generalization, a closely related ML area aimed at building robust models able to generalize well across multiple data distributions (environments).

Class info

Classes are online from January 14 to April 15, 2021


  • Instructor: Irina Rish (irina.rish at

  • Teaching Assistants:

    • Mojtaba Faramarzi (faramarm at

    • Touraj Laleh (tourajla at

Course Structure and Goals

This is an advanced, seminar-style machine learning course that combines lectures with student presentations of recent papers on continual learning, as well as related fields, e.g., out-of-distribution generalization and meta-learning. This will be a project-driven course, where instead of homework and exams students are expected to:

  • Present papers on continual learning and/or out-of-distribution generalization topics: each student will present 2 papers (with a partner) and lead the discussion about these papers (1 hr presentation + discussion).

  • Participate in discussions, submit questions on course slack (1 question per student/per paper).

  • Submit 1-2 page project proposal by mid-semester

  • Give a poster presentation of the project at the end of the course

  • Submit written project report (NeurIPS style paper)

Evaluation Criteria

  • Paper presentations: 40%

  • Class project (report + poster presentation): 50%

  • Class participation: asking questions, participating in discussions (on slack/in class): 10%

Note: due to time zone differences, it may be difficult for all students to join all classes in person; the classes will be recorded, and questions regarding the papers to be discussed can be submitted on the course slack.