A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
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Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
NIE-PML Personalized Machine Learning Extent of teaching: 2P+1C Instructor: Da Silva Alves R. Completion: Z,ZK Department: 18105 Credits: 5 Semester: Z Annotation:
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.
Lecture syllabus:
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 Seminar syllabus:
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.Literature:
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 Requirements:
The knowledge of calculus, linear algebra, probability theory and basics of machine learning is assumed.
Information about course and coursware are available at https://courses.fit.cvut.cz/NIE-PML/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NI-TI.2018 Computer Science V Není NIE-SI.21 Software Engineering 2021 V Není NIE-TI.21 Computer Science 2021 V Není NIE-DBE.2023 Digital Business Engineering V Není NIE-NPVS.21 Design and Programming of Embedded Systems 2021 V Není NIE-PSS.21 Computer Systems and Networks 2021 V Není NIE-PB.21 Computer Security 2021 V Není NI-PB.2020 Computer Security V Není NI-ZI.2020 Knowledge Engineering V Není NI-SPOL.2020 Unspecified Branch/Specialisation of Study V Není NI-TI.2020 Computer Science V Není NI-TI.2023 Computer Science V Není NI-NPVS.2020 Design and Programming of Embedded Systems V Není NI-PSS.2020 Computer Systems and Networks V Není NI-MI.2020 Managerial Informatics V Není NI-SI.2020 Software Engineering (in Czech) V Není NI-SP.2020 System Programming V Není NI-WI.2020 Web Engineering V Není NI-SP.2023 System Programming V Není
Page updated 20. 4. 2024, semester: L/2023-4, L/2020-1, L/2022-3, L/2021-2, Z/2019-20, Z/2022-3, Z/2020-1, Z/2023-4, L/2019-20, Z/2021-2, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška