Recommender Systems: Behind the Screen (edX)

Recommender Systems: Behind the Screen (edX)
Course Auditing
Categories
Effort
Certification
Languages
Minimal knowledge of programming (ideally in Python) and basic (first year undergraduate) knowledge in mathematics (linear algebra, statistics).
Misc

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Recommender Systems: Behind the Screen (edX)
How are items recommended when you’re browsing for movies, jobs or clothing online? Register here and you’ll discover the fundamental concepts and methods allowing the most relevant item suggestions to users from e-commerce to online advertisement. 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.

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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.


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.


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



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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

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

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.