Building Recommender Systems: Fundamentals and Practice
Learn to design and implement personalized recommendation algorithms for e-commerce and online platforms.
Learn to design and implement personalized recommendation algorithms for e-commerce and online platforms.
This comprehensive course explores the fundamentals of recommender systems, developed by IVADO and HEC Montréal. Led by seven international experts, students learn essential algorithms and techniques for creating personalized recommendation systems. The curriculum covers machine learning applications, evaluation methods, advanced modeling, and ethical considerations in recommendation systems. Through practical tutorials and hands-on exercises in Python, participants gain real-world experience in implementing recommendation algorithms for various applications.
Instructors:
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What you'll learn
Master core concepts and terminology of recommender systems
Identify appropriate recommendation methods for specific problems
Implement recommendation algorithms using Python
Evaluate and optimize recommender system performance
Understand advanced modeling techniques and neural networks
Apply contextual bandits and learning-to-rank methods
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course provides a comprehensive introduction to recommender systems, essential components of modern online platforms. The curriculum covers fundamental concepts and advanced techniques in recommendation algorithms, including machine learning applications, evaluation methods, and ethical considerations. Students learn through a combination of theoretical instruction and practical implementation, with hands-on tutorials in Python. The course emphasizes both technical proficiency and understanding of real-world applications, preparing participants to develop effective recommendation systems for various platforms.
Machine Learning for Recommender Systems
Module 1
Evaluations for Recommender Systems
Module 2
Advanced modelling
Module 3
Contextual Bandits
Module 4
Learning to Rank
Module 5
Fairness and Discrimination in Recommender Systems
Module 6
Fee Structure
Instructors
3 Courses
Distinguished Machine Learning Expert and AI Researcher
Laurent Charlin serves as Associate Professor at HEC Montréal and Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute. After completing his PhD from the University of Toronto, MMath from the University of Waterloo, and postdoctoral work at Princeton, Columbia, and McGill Universities, he has established himself as a leading researcher in machine learning and decision-making systems. His research focuses on developing novel machine learning models, particularly in recommender systems, dialogue systems, and optimization. His most significant contribution includes co-developing the Toronto Paper Matching System (TPMS), widely adopted by computer science conferences for reviewer assignment. His recent work explores continual learning, with applications in fields such as recommender systems and optimization. As a core academic member of Mila and professor at HEC Montréal's Department of Decision Sciences, he continues to advance the field through research in Bayesian statistics, deep learning, and artificial intelligence while contributing to AI literacy through MOOCs and public engagement.
2 Courses
Distinguished AI Ethics Expert and Information Systems Pioneer
Fernando Diaz serves as Research Scientist at Google and holds a CIFAR AI Chair at Mila. After completing his PhD from the University of Massachusetts Amherst and BS from the University of Michigan, he has established himself as a leading researcher in information retrieval and AI ethics. His career includes significant roles as Assistant Managing Director at Microsoft Research Montreal, where he led the FATE (Fairness, Accountability, Transparency and Ethics in AI) research group, and Director of Research at Spotify, where he helped establish their research organization. His work has earned numerous accolades, including the 2017 British Computer Society Karen Spärck Jones Award and special recognition at major conferences including SIGIR, CIKM, and WSDM. His research focuses on developing ethical AI systems, particularly in information access and recommendation systems, while addressing their societal implications. Through his leadership in organizing key conferences and workshops in AI ethics and information retrieval, he continues to shape the discourse on responsible AI development.
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