Dans ce cours, vous explorerez et apprendrez les meilleures méthodes et pratiques des systèmes de recommandation, qui sont une composante essentielle de l’écosystème digital. Ce cours a été développé par IVADO et HEC Montréal dans le cadre d’un atelier qui a eu lieu à Montréal. Vous serez accompagné.e tout au long du processus par sept experts internationaux du milieu universitaire et de l’industrie qui vous fourniront des exemples concrets.
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Dans ce cours, vous explorerez et apprendrez les meilleures méthodes et pratiques des systèmes de recommandation, qui sont une composante essentielle de l’écosystème digital. Ce cours a été développé par IVADO et HEC Montréal dans le cadre d’un atelier qui a eu lieu à Montréal. Vous serez accompagné.e tout au long du processus par sept experts internationaux du milieu universitaire et de l’industrie qui vous fourniront des exemples concrets.
Dans ce cours, vous explorerez et apprendrez les meilleures méthodes et pratiques des systèmes de recommandation, qui sont une composante essentielle de l’écosystème digital. Ce cours a été développé par IVADO et HEC Montréal dans le cadre d’un atelier qui a eu lieu à Montréal. Vous serez accompagné.e tout au long du processus par sept experts internationaux du milieu universitaire et de l’industrie qui vous fourniront des exemples concrets.
Les systèmes de recommandation sont des algorithmes qui trouvent des tendances dans le comportement des utilisateurs pour améliorer les expériences personnalisées et comprendre leur environnement. Ils sont omniprésents et sont le plus souvent utilisés pour recommander des items aux utilisateurs, notamment des livres et des films, mais aussi des amis potentiels, des recettes de cuisine ou même de la documentation pertinente pour de grands projets de logiciels, ou des articles intéressant les scientifiques.
Le contenu de ce MOOC est une introduction au domaine des systèmes de recommandation. Le programme comprend : l’apprentissage automatique pour les systèmes de recommandation, suivi d’une introduction aux méthodes d’évaluation; la modélisation avancée; les bandits contextuels; les méthodes de classement; l’équité et la discrimination dans les systèmes de recommandation.
Le cours s’adresse principalement aux professionnel.le.s du secteur et aux étudiant.e.s universitaires ayant des connaissances de base (première année de baccalauréat) en mathématiques et en programmation (idéalement Python). Les diplômé.e.s en sciences et en ingénierie (principalement celles et ceux qui ne sont pas encore familiers avec l’apprentissage automatique et les systèmes de recommandation) trouveront ce contenu instructif et intéressant. Le contenu de ce cours sera également d’une grande utilité pour toute personne qui s’intéresse à l’IA ou qui l’utilise, de quelque manière que ce soit.
Nous estimons qu’il faut 6 semaines pour compléter ce cours. Le cours est divisé en segments que vous pourrez visionner à votre propre rythme. Afin d’évaluer votre compréhension du contenu, des questionnaires complets seront proposés à la fin de chaque segment. Vous pourrez également pratiquer les algorithmes des systèmes de recommandation grâce à un tutoriel guidé par un expert. De plus, un deuxième module de pratique individuelle sera offert aux participant.e.s qui s’inscrivent au cours avec le certificat vérifié.
Nous vous souhaitons la bienvenue dans ce parcours d’apprentissage de Systèmes de recommandation : Derrière l’écran!
Ce cours vous est présenté par IVADO, HEC Montréal et l’Université de Montréal.
IVADO est un institut collaboratif québécois dans le domaine de l’intelligence numérique.
HEC Montréal est une université francophone de renommée internationale qui offre des formations et des programmes de recherche dans le domaine de la gestion.
L’Université de Montréal est l’une des principales universités de recherche au monde.
What's inside
Learning objectives
- À la fin du mooc, les participant devraient être en mesure de :
- Comprendre les bases des systèmes de recommandation, y compris leur terminologie ;
- Identifier les types de problèmes et les méthodes des systèmes de recommandation pour les résoudre ;
- Appliquer la méthodologie de réalisation d'un projet en systèmes de recommandation ;
- Utiliser les algorithmes des systèmes de recommandation à travers des sessions pratiques et des tutoriels.
- À la fin du mooc, les participant devraient être en mesure de :
- Comprendre les bases des systèmes de recommandation, y compris leur terminologie ;
- Identifier les types de problèmes et les méthodes des systèmes de recommandation pour les résoudre ;
- Appliquer la méthodologie de réalisation d'un projet en systèmes de recommandation ;
- Utiliser les algorithmes des systèmes de recommandation à travers des sessions pratiques et des tutoriels.
Syllabus
MODULE 1 Apprentissage automatique pour les systèmes de recommandation
Modèles basés sur le score
Aspects pratiques
MODULE TUTORIEL : Factorisation matricielle
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Syllabus
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Activities
Organize Course Materials
Show steps
Prepare for the course by gathering and organizing all necessary materials.
Show steps
-
Download and print lecture notes.
-
Gather textbooks and online resources.
-
Create a dedicated study space.
Review basic algebra skills
Show steps
Helps with understanding the mathematical foundations of recommendation systems.
Browse courses on
Linear Algebra
Show steps
-
Review basic operations like addition, subtraction, multiplication, and division.
-
Practice solving linear equations.
-
Review basic matrix operations.
Review ML and probability
Show steps
Review the basics of machine learning and probability to ensure a solid foundation for this course.
Browse courses on
Machine Learning Basics
Show steps
-
Revisit fundamental concepts of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.
-
Recall basic probability concepts, such as conditional probability, Bayes' theorem, and random variables.
17 other activities
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Show all 20 activities
Review Machine Learning Fundamentals
Show steps
Strengthen your foundation in Machine Learning before starting the course.
Show steps
-
Revisit key concepts in Machine Learning.
-
Solve practice problems or take quizzes.
-
Refresh your understanding of supervised and unsupervised learning.
Participate in online discussion forums
Show steps
Engage with peers in online discussion forums to exchange ideas, ask questions, and deepen understanding of course material.
Show steps
-
Identify relevant discussion forums or online communities related to recommender systems.
-
Actively participate in discussions, sharing your perspectives and responding to others.
Practice Matrix Factorization
Show steps
Reinforce your understanding of Matrix Factorization through solving practice problems.
Show steps
-
Review the notes on Matrix Factorization.
-
Solve practice problems on your own.
-
Compare your answers with the solutions provided.
Participate in a study group or online discussion forum on recommendation systems
Show steps
Provides opportunities to engage with peers, ask questions, and reinforce learning.
Browse courses on
Recommendation Systems
Show steps
-
Find or join a study group or online discussion forum.
-
Participate in discussions and ask questions.
-
Share your knowledge and insights with others.
Complete Tensorflow tutorial
Show steps
Follow a guided tutorial on Tensorflow to gain hands-on experience with a deep learning framework.
Browse courses on
TensorFlow
Show steps
-
Set up a development environment with Python and Tensorflow.
-
Walk through the basics of Tensorflow, including tensors, operations, and models.
-
Build and train a simple neural network using Tensorflow.
Follow a tutorial on matrix factorization
Show steps
Provides hands-on experience with a fundamental technique used in recommendation systems.
Browse courses on
Matrix Factorization
Show steps
-
Find an online tutorial or resource on matrix factorization.
-
Follow the steps in the tutorial to implement matrix factorization.
-
Test your implementation on a small dataset.
Solve coding challenges
Show steps
Engage in coding challenges to strengthen problem-solving skills and refine coding abilities.
Browse courses on
Coding Challenges
Show steps
-
Identify reputable online platforms or resources for coding challenges.
-
Select challenges that align with the topics covered in this course.
-
Attempt to solve the challenges independently, referring to course materials when necessary.
TensorFlow Tutorial for Recommendation Systems
Show steps
Deepen your knowledge of Recommendation Systems by following a guided tutorial.
Browse courses on
TensorFlow
Show steps
-
Find a TensorFlow tutorial for Recommendation Systems.
-
Follow the tutorial step-by-step.
-
Explore the code and understand how it works.
-
Implement the model on a small dataset.
Discussion Group on Fairness in Recommendation Systems
Show steps
Engage in discussions to explore the ethical implications and challenges of Recommendation Systems.
Browse courses on
Recommendation Systems
Show steps
-
Join a discussion group or forum.
-
Participate in discussions on fairness in Recommendation Systems.
-
Share your perspectives and learn from others.
Participate in Kaggle competitions
Show steps
Engage in Kaggle competitions to apply your skills, learn from others, and contribute to the broader data science community.
Browse courses on
Kaggle Competitions
Show steps
-
Identify Kaggle competitions that align with your interests and skill level.
-
Form a team or work independently to develop solutions to the competition challenges.
-
Submit your solutions and track your progress on the leaderboard.
Contribute to Open Recommendation Systems Projects
Show steps
Gain hands-on experience and contribute to the Recommendation Systems community.
Browse courses on
Open Source
Show steps
-
Identify open source Recommendation Systems projects.
-
Choose a project to contribute to.
-
Understand the project's codebase and requirements.
-
Make bug fixes or implement new features.
Solve practice problems on recommendation algorithms
Show steps
Reinforces understanding of the algorithms and techniques used in recommendation systems.
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems on your own.
-
Check your solutions against provided answers or consult with an expert.
Build a recommender system
Show steps
Develop a recommender system project to apply the concepts learned in the course and showcase your skills.
Browse courses on
Recommender Systems
Show steps
-
Define the problem statement and gather relevant data.
-
Choose an appropriate machine learning algorithm and train a model.
-
Evaluate the performance of your model and make necessary adjustments.
-
Deploy your recommender system and monitor its performance.
Implement a Recommendation System for a Movie Website
Show steps
Apply your knowledge by building a real-world Recommendation System.
Browse courses on
Recommendation Systems
Show steps
-
Choose a movie dataset.
-
Select and implement a suitable recommendation algorithm.
-
Build a web interface to interact with your system.
-
Evaluate your system's performance.
Contribute to open-source recommender system projects
Show steps
Gain practical experience and contribute to the open-source community by participating in recommender system projects.
Browse courses on
Open Source Projects
Show steps
-
Explore existing open-source recommender system projects on platforms like GitHub.
-
Identify a project that aligns with your interests and skills.
-
Fork the project and make contributions, such as bug fixes, feature enhancements, or documentation updates.
Write a blog post or article on a topic related to recommendation systems
Show steps
Encourages critical thinking and deepens understanding by requiring students to explain concepts in their own words.
Browse courses on
Recommendation Systems
Show steps
-
Choose a specific topic related to recommendation systems.
-
Research the topic thoroughly.
-
Write a well-structured and informative blog post or article.
-
Publish your content online.
Build a simple recommendation system using open-source libraries
Show steps
Provides practical experience in implementing and evaluating recommendation systems.
Browse courses on
Recommendation Systems
Show steps
-
Choose a dataset and import it into your preferred programming environment.
-
Select and implement a recommendation algorithm using open-source libraries.
-
Evaluate the performance of your recommendation system.
-
Refine and improve your system based on the evaluation results.
Organize Course Materials
Show steps
Prepare for the course by gathering and organizing all necessary materials.
Show steps
- Download and print lecture notes.
- Gather textbooks and online resources.
- Create a dedicated study space.
Review basic algebra skills
Show steps
Helps with understanding the mathematical foundations of recommendation systems.
Browse courses on
Linear Algebra
Show steps
- Review basic operations like addition, subtraction, multiplication, and division.
- Practice solving linear equations.
- Review basic matrix operations.
Review ML and probability
Show steps
Review the basics of machine learning and probability to ensure a solid foundation for this course.
Browse courses on
Machine Learning Basics
Show steps
- Revisit fundamental concepts of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.
- Recall basic probability concepts, such as conditional probability, Bayes' theorem, and random variables.
Review Machine Learning Fundamentals
Show steps
Strengthen your foundation in Machine Learning before starting the course.
Show steps
- Revisit key concepts in Machine Learning.
- Solve practice problems or take quizzes.
- Refresh your understanding of supervised and unsupervised learning.
Participate in online discussion forums
Show steps
Engage with peers in online discussion forums to exchange ideas, ask questions, and deepen understanding of course material.
Show steps
- Identify relevant discussion forums or online communities related to recommender systems.
- Actively participate in discussions, sharing your perspectives and responding to others.
Practice Matrix Factorization
Show steps
Reinforce your understanding of Matrix Factorization through solving practice problems.
Show steps
- Review the notes on Matrix Factorization.
- Solve practice problems on your own.
- Compare your answers with the solutions provided.
Participate in a study group or online discussion forum on recommendation systems
Show steps
Provides opportunities to engage with peers, ask questions, and reinforce learning.
Browse courses on
Recommendation Systems
Show steps
- Find or join a study group or online discussion forum.
- Participate in discussions and ask questions.
- Share your knowledge and insights with others.
Complete Tensorflow tutorial
Show steps
Follow a guided tutorial on Tensorflow to gain hands-on experience with a deep learning framework.
Browse courses on
TensorFlow
Show steps
- Set up a development environment with Python and Tensorflow.
- Walk through the basics of Tensorflow, including tensors, operations, and models.
- Build and train a simple neural network using Tensorflow.
Follow a tutorial on matrix factorization
Show steps
Provides hands-on experience with a fundamental technique used in recommendation systems.
Browse courses on
Matrix Factorization
Show steps
- Find an online tutorial or resource on matrix factorization.
- Follow the steps in the tutorial to implement matrix factorization.
- Test your implementation on a small dataset.
Solve coding challenges
Show steps
Engage in coding challenges to strengthen problem-solving skills and refine coding abilities.
Browse courses on
Coding Challenges
Show steps
- Identify reputable online platforms or resources for coding challenges.
- Select challenges that align with the topics covered in this course.
- Attempt to solve the challenges independently, referring to course materials when necessary.
TensorFlow Tutorial for Recommendation Systems
Show steps
Deepen your knowledge of Recommendation Systems by following a guided tutorial.
Browse courses on
TensorFlow
Show steps
- Find a TensorFlow tutorial for Recommendation Systems.
- Follow the tutorial step-by-step.
- Explore the code and understand how it works.
- Implement the model on a small dataset.
Discussion Group on Fairness in Recommendation Systems
Show steps
Engage in discussions to explore the ethical implications and challenges of Recommendation Systems.
Browse courses on
Recommendation Systems
Show steps
- Join a discussion group or forum.
- Participate in discussions on fairness in Recommendation Systems.
- Share your perspectives and learn from others.
Participate in Kaggle competitions
Show steps
Engage in Kaggle competitions to apply your skills, learn from others, and contribute to the broader data science community.
Browse courses on
Kaggle Competitions
Show steps
- Identify Kaggle competitions that align with your interests and skill level.
- Form a team or work independently to develop solutions to the competition challenges.
- Submit your solutions and track your progress on the leaderboard.
Contribute to Open Recommendation Systems Projects
Show steps
Gain hands-on experience and contribute to the Recommendation Systems community.
Browse courses on
Open Source
Show steps
- Identify open source Recommendation Systems projects.
- Choose a project to contribute to.
- Understand the project's codebase and requirements.
- Make bug fixes or implement new features.
Solve practice problems on recommendation algorithms
Show steps
Reinforces understanding of the algorithms and techniques used in recommendation systems.
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems on your own.
- Check your solutions against provided answers or consult with an expert.
Build a recommender system
Show steps
Develop a recommender system project to apply the concepts learned in the course and showcase your skills.
Browse courses on
Recommender Systems
Show steps
- Define the problem statement and gather relevant data.
- Choose an appropriate machine learning algorithm and train a model.
- Evaluate the performance of your model and make necessary adjustments.
- Deploy your recommender system and monitor its performance.
Implement a Recommendation System for a Movie Website
Show steps
Apply your knowledge by building a real-world Recommendation System.
Browse courses on
Recommendation Systems
Show steps
- Choose a movie dataset.
- Select and implement a suitable recommendation algorithm.
- Build a web interface to interact with your system.
- Evaluate your system's performance.
Contribute to open-source recommender system projects
Show steps
Gain practical experience and contribute to the open-source community by participating in recommender system projects.
Browse courses on
Open Source Projects
Show steps
- Explore existing open-source recommender system projects on platforms like GitHub.
- Identify a project that aligns with your interests and skills.
- Fork the project and make contributions, such as bug fixes, feature enhancements, or documentation updates.
Write a blog post or article on a topic related to recommendation systems
Show steps
Encourages critical thinking and deepens understanding by requiring students to explain concepts in their own words.
Browse courses on
Recommendation Systems
Show steps
- Choose a specific topic related to recommendation systems.
- Research the topic thoroughly.
- Write a well-structured and informative blog post or article.
- Publish your content online.
Build a simple recommendation system using open-source libraries
Show steps
Provides practical experience in implementing and evaluating recommendation systems.
Browse courses on
Recommendation Systems
Show steps
- Choose a dataset and import it into your preferred programming environment.
- Select and implement a recommendation algorithm using open-source libraries.
- Evaluate the performance of your recommendation system.
- Refine and improve your system based on the evaluation results.
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