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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Bayesian Methods for Machine Learning

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Code Completion Credits Range Language
MI-BML KZ 5 2+1 Czech
Lecturer:
Ondřej Tichý (guarantor), Kamil Dedecius (guarantor)
Tutor:
Ondřej Tichý (guarantor), Kamil Dedecius (guarantor)
Supervisor:
Department of Applied Mathematics
Synopsis:

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.

Requirements:
Syllabus of lectures:

1. Basics and details of the Bayesian theory - uncertainty, knowledge evolution, types of

estimates, methods.

2. Basic terms in machine learning from the Bayesian viewpoint, regression, classification,

examples.

3. Linear model, existence of analytical solution, structure estimation, regularization via prior,

examples.

4. Sequential (online) estimation of linear models, prediction, examples.

5. Generalized linear models, approximations and sequential (online) estimation.

6. Application of generalized linear models in real machine learning problems.

7. Bilinear models and Bayesian approach to PCA, estimation of the number of components.

8. Basic state-space models, Kalman filter.

9. Sequential Monte Carlo estimation of state-space models, bootstrap particle filter.

10. Problems of sequential Monte Carlo filtration, resampling and alternatives.

11. Hierarchical learning and its applications.

12. Introduction to deep learning and probabilistic graphical models.

Syllabus of tutorials:

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. Construction of a state-space model for a real world problem and its estimation.

5. Bayesian matrix decomposition problem and its application, e.g., in biomedicine.

6. Particle filtration in practical problems of machine learning.

Study Objective:
Study materials:

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.

Note:
Time-table for winter semester 2018/2019:
Time-table is not available yet
Time-table for summer semester 2018/2019:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
roomT9:347
Dedecius K.
09:15–10:45
(lecture parallel1)
Dejvice
NBFIT učebna
roomT9:348
Dedecius K.
Tichý O.

11:00–12:30
EVEN WEEK

(lecture parallel1
parallel nr.101)

Dejvice
NBFIT PC ucebna
Tue
Fri
Thu
Fri
The course is a part of the following study plans:
Data valid to 2019-02-18
For updated information see http://bilakniha.cvut.cz/en/predmet5423206.html