Personalized Machine Learning

Login to KOS for course enrollment Display time-table
Code Completion Credits Range Language
NIE-PML Z,ZK 5 2P+1C English
Garant předmětu:
Rodrigo Augusto Da Silva Alves
Rodrigo Augusto Da Silva Alves
Rodrigo Augusto Da Silva Alves
Department of Applied Mathematics

Personalized machine learning (PML) is a sub-field of machine learning that aims to create models and predictions based on the unique characteristics and behaviors of individual entities. While PML is commonly used in applications such as recommender systems, which recommend items to users based on their personal interests, its principles can be applied to a wide range of other fields, including education, medicine, and chemical engineering. In this course, we will explore the latest PML methods from theoretical, algorithmic, and practical perspectives. Specifically, we will focus on cutting-edge models that are of interest to both the research and commercial communities.


The knowledge of calculus, linear algebra, probability theory and basics of machine learning is assumed.

Syllabus of lectures:

1. Introduction to Personalized Machine Learning and its fundamental tools.

2. Overview of Recommender Systems and their importance in personalized machine learning.

3. Model-based approaches for Recommender Systems

4. Content-based Recommendation

5. Temporal and Sequential models

6. Cross-domain models

7. Personalized models of Text

8. Visual Personalized Models

9. Emerging trends in Personalized Machine Learning

10. Ethical Aspects of Personalized Models

Syllabus of tutorials:

The course exercises will be designed to help students develop a comprehensive understanding of personalized models, from both applied and fundamental research perspectives. These exercises will be structured in a series of steps, each contributing to building a solid framework for creating a personalized machine learning model. Students will focus on implementing the concepts learned in real-world scenarios, which will culminate in a substantial project resulting in a scientific paper. This approach will allow students to gain practical experience with the techniques and tools used in the field, and demonstrate their ability to apply them to real-world problems. Throughout the course, students will receive guidance and support from their instructor and peers, helping them to stay on track and achieve their goals in a student-centered methodology.

Study Objective:

This course is designed for students seeking an advanced understanding of personalized machine learning methods and a practical introduction to applied and/or fundamental research in the field. The course is also suitable for those seeking an initial foray into research. By the conclusion of the course, students are expected to have developed a thorough understanding of personalized machine learning models and the practical skills and knowledge necessary for developing such models in research and commercial contexts. Moreover, it is expected that the course project should yield to a scientific paper (without the need of submission) or a practical solution that can be publicly shared and added to the student's portfolio.

Study materials:

1. McAuley, J ., Personalized Machine Learning. Cambridge University Press, 2022. ISBN: 978-1316518908

2. Aggarwal, Ch. C. , Recommender Systems. Springer, 2016. ISBN 978-3319296579.

3. James, G., Witten, D., Hastie, T., & Tibshirani, R. An introduction to statistical learning. New York: springer, 2013. ISBN: 978-1461471370

4. Deisenroth, M. P., Faisal, A. A ONG, Cheng Soon, Mathematics for machine learning. Cambridge University Press, 2020. ISBN: 978-1108455145

Further information:
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-06-16
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet7580806.html