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Personalized Machine Learning

Přihlášení do KOSu pro zápis předmětu Zobrazit rozvrh
Kód Zakončení Kredity Rozsah Jazyk výuky
NIE-PML Z,ZK 5 2P+1C anglicky
Garant předmětu:
Rodrigo Augusto Da Silva Alves
Rodrigo Augusto Da Silva Alves
Rodrigo Augusto Da Silva Alves
Předmět zajišťuje:
katedra aplikované matematiky

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.

Osnova přednášek:

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

Osnova cvičení:

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.

Cíle studia:

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.

Studijní materiály:

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


Information about course and coursware are available at https://courses.fit.cvut.cz/NIE-PML/

Další informace:
Rozvrh na zimní semestr 2023/2024:
místnost T9:301
Da Silva Alves R.
(přednášková par. 1)
NBFIT učebna
místnost T9:348
Da Silva Alves R.

(přednášková par. 1
paralelka 101)

NBFIT PC ucebna

Rozvrh na letní semestr 2023/2024:
Rozvrh není připraven
Předmět je součástí následujících studijních plánů:
Platnost dat k 24. 5. 2024
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/cs/predmet7580806.html