• Length:
    6 Weeks
  • Effort:
    4–6 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Introductory
  • Language:
    English
  • Video Transcript:
    English
  • Course Type:
    Self-paced on your time

Prerequisites

Minimal knowledge of programming (ideally in Python) and basic (first year undergraduate) knowledge in mathematics (linear algebra, statistics).

About this course

Skip About this course

In this course, you will explore and learn the best methods and practices in recommender systems, which are an essential component of the online ecosystem. This course was developed by IVADO and HEC Montréal as part of a workshop that took place in Montreal. You will be accompanied throughout and given concrete examples by seven international experts from both Academia and Industry.

Recommender systems are algorithms that find patterns in user behaviour to improve personalized experiences and understand their environment. They are ubiquitous and are most often used to recommend items to users, for example, books, movies, but also possible friends, food recipes or even relevant documentation in large software projects, or papers of interest to scientists.

The content of this MOOC is an introduction to the field of recommender systems. The outline includes: machine learning for recommender systems followed by an introduction to evaluation methods; advanced modelling; contextual bandits; ranking methods; and fairness and discrimination in recommender systems.

The course is primarily intended for industry professionals and academics with basic (first-year undergraduate) knowledge in mathematics and programming (ideally Python). Graduate students in science and engineering (mainly those who are not yet familiar with machine learning and recommender systems) may find this content instructive and compelling. The content of this course will also be of great use to whomever uses or is interested in AI, in any other way.

We estimate that it takes 6 weeks to follow this class. The course is divided into relevant segments that you may watch at your own pace. There are comprehensive quizzes at the end of each segment to evaluate your understanding of the content. You will also practice recommender systems algorithms thanks to a tutorial guided by an expert. Also, a second self-practice module will be offered to participants who will register for the course with the Verified Certificate.

We welcome you to this special learning journey of Recommender Systems: Behind the Screen!

This course is brought to you by IVADO, HEC Montréal and Université de Montréal.

IVADO is a Québec-wide collaborative institute in the field of digital intelligence.

HEC Montréal is a French-language university offering internationally renowned management education and research.

Université de Montréal is one of the world’s leading research universities.

What you'll learn

Skip What you'll learn

At the end of the MOOC, participants should be able to:

  • Understand the basics of recommender systems including its terminology;
  • Identify the types of problems and the recommender systems’ methods to solve those;
  • Apply the methodology for carrying out a project in recommender systems;
  • Use recommender systems’ algorithms through practical and tutorial sessions.

MODULE 1 Machine Learning for Recommender Systems

  • Score Models
  • Practical Aspects

MODULE TUTORIAL Matrix Factorization

MODULE 2 Evaluations for Recommender Systems

  • Offline (Batch) Evaluation
  • Online (Production) Evaluation

MODULE 3 Advanced modelling

  • Extending Basic Models
  • A missing Data Perspective

MODULE SELF-PRACTICE Autoencoders (this module is assessed and offered only to participants who register for the course with the Verified Certificate)

MODULE 4 Contextual Bandits

  • Introduction to Bandits
  • Putting it All Together

MODULE 5 Learning to Rank

  • Learning to Rank with Neural Networks
  • Learning to Rank with Deep Neural Networks

MODULE 6 Fairness and Discrimination in Recommender Systems

  • Algorithmic Fairness
  • Fairness in Information Retrieval

Meet your instructors

Laurent CHARLIN
Ph.D. Assistant Professor, Department of Decision Sciences, HEC Montreal Member of Mila – Quebec Artificial Intelligence Institute
HEC Montreal, Mila
Fernando DIAZ
Ph.D, Research Scientist, Google | Member of Mila – Quebec Artificial Intelligence Institute
Google, Mila
Michael EKSTRAND
Ph.D. Assistant Professor, Department of Computer Science, Boise State University
Boise State University
Dora JAMBOR
Graduate student in Computer Science, McGill University Researcher at Mila, the Quebec Artificial Intelligence Institute
McGill University, Mila

Course created with support from

IVADOHEC Montréal

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Frequently asked questions

What is the complete list of speakers for this course?

Laurent CHARLIN

Fernando DIAZ

Michael EKSTRAND

Dora JAMBOR

Dawen LIANG

James McINERNEY

Bhaskar MITRA