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Online Course on Recommender Systems- Behind the Screen by Université de Montréal [6 Weeks]: Enroll Now

Université de Montréal offers 6 weeks Online Course on Recommender Systems- Behind the Screen. Online registrations are open.

About

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.

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.
Prerequisites

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

Syllabus

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

To enroll in this program, click the link below.

Online Course on Recommender Systems- Behind the Screen

Note: Noticebard is associated with edX through an affiliate programme.

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