Recommender Systems: Behind the Screen
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.
Upcoming start dates
- Self-paced Online
Who should attend?
Minimal knowledge of programming (ideally in Python) and basic (first year undergraduate) knowledge in mathematics (linear algebra, statistics).
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
Course delivery details
This course is offered through Université de Montréal, a partner institute of EdX.
4-6 hours per week
- Verified Track -$150
- Audit Track - Free
Certification / Credits
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.
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