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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.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
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.

MI-BML Bayesian Methods for Machine Learning Extent of teaching: 2P+1C
Instructor: Completion: KZ
Department: 18105 Credits: 5 Semester: L

Annotation:
The subject is focused on practical use of basic Bayesian modeling methods in the dynamically evolving machine learning theory. In particular, it studies the construction of appropriate models providing description of real phenomena, as well as their subsequent use, e.g., for forecasting of future evolution or learning about the hidden variables (true object position from noisy observations etc.). The emphasis is put on understanding of explained principles and methods and their practical adoption. For this purpose, a number of real world examples and applications will be presented to students, for instance, 2D/3D object tracking, radiation source term estimation, or separation in medical imaging. The students will try to solve some of them.

Lecture syllabus:
1. Basics and details of the Bayesian theory - uncertainty, knowledge evolution, types of estimates, methods.
2. Linear models in machine learning, online modeling, prediction, examples.
3. Generalized linear models GLM, approximation and sequential (online) estimation.
4. Linear model, structure estimation, prior-based regularization.
5. Bilinear models and Bayesian approach to PCA, estimation of the number of components.
6. Application of generalized linear models in real machine learning problems.
7. Basic state-space models, Kalman filter.
8. Introduction into Monte Carlo methods, rejection sampling.
9. Sequential Monte Carlo estimation of state-space models, bootstrap particle filter, resampling.
10. Hierarchical learning and its applications.
11. Graphical models, naive Bayes.
12. Introduction to deep learning and probabilistic graphical models.

Seminar syllabus:
1. Introduction, construction of a linear model and its estimation, knowledge evolution, forecasting.
2. Bayesian sequential linear regression, regularization, demonstrations on real data.
3. Sequential logistic regression with real data.
4. Bayesian matrix decomposition problem and its application, e.g., in biomedicine.
5. Construction of a state-space model for a real world problem and its estimation.
6. Particle filtration in practical problems of machine learning.

Literature:
1. Andrew Gelman et al., Bayesian Data Analysis, Chapman and Hall (2013), ISBN 1439840954.
2. David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), ISBN 978-0-521-51814-7.

Requirements:
Basic knowledge of probability theory and linear algebra.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-BML/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
MI-ZI.2016 Knowledge Engineering V 4
MI-ZI.2018 Knowledge Engineering V 4
MI-SP-TI.2016 System Programming V 4
MI-SP-SP.2016 System Programming V 4
MI-SPOL.2016 Unspecified Branch/Specialisation of Study V 4
MI-WSI-WI.2016 Web and Software Engineering V 4
MI-WSI-SI.2016 Web and Software Engineering V 4
MI-WSI-ISM.2016 Web and Software Engineering V 4
MI-NPVS.2016 Design and Programming of Embedded Systems V 4
MI-PSS.2016 Computer Systems and Networks V 4
MI-PB.2016 Computer Security V 4
NI-TI.2018 Computer Science V 2


Page updated 29. 3. 2024, semester: L/2021-2, Z,L/2023-4, Z/2021-2, Z/2020-1, Z/2019-20, L/2020-1, Z,L/2022-3, L/2019-20, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška