Statistical Modelling Lab
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
NI-LSM | KZ | 5 | 3C | Czech |
- Garant předmětu:
- Kamil Dedecius
- Lecturer:
- Kamil Dedecius
- Tutor:
- Kamil Dedecius
- Supervisor:
- Department of Applied Mathematics
- Synopsis:
-
The subject is oriented on a low-level approach to Bayesian statistical and information-theoretical modelling, where the student both learns the existing methods (regression models, Kalman filtering, models fusion, etc.) and tries to implement them. That is, instead of the (standard) intensive use of high-level libraries like pandas, scikit-learn or statsmodels, the stress is put on the use of numpy and scipy, as well as the low-level algebra and calculus. The second half of the semester is focused on the design of methods and algorithms, and analyses of their properties. At this point, the subject is on the border of own research and may result in the topic of final work (diploma or bachelor thesis).
- Requirements:
-
BI-LIN, BI-ZMA
Ideally BI-PST too.
- Syllabus of lectures:
-
1. Introduction into statistical modelling, Bayesian approach.
2. Linear model, prior and posterior information.
3. Sequential estimation, linear regression, varying parameters.
4. State-space models, filtering of states.
5. Model mixing and probabilistic mixtures.
6. Information divergences, Kullback-Leibler divergence, its use.
7. Project: Assignment.
8. Project: Analysis of the state of the art.
9. Project: Design of suitable solutions.
10. Project: Implementation of proposed solutions.
11. Project: Analysis of results.
12. Project: Assessment
- Syllabus of tutorials:
-
1. Introduction into statistical modelling, Bayesian approach.
2. Linear model, prior and posterior information.
3. Sequential estimation, linear regression, varying parameters.
4. State-space models, filtering of states.
5. Míchání modelů a pravděpodobnostní směsi.
6. Information divergences, Kullback-Leibler divergence, its use.
7. Project: Assignment.
8. Project: Analysis of the state of the art.
9. Project: Design of suitable solutions.
10. Project: Implementation of proposed solutions.
11. Project: Analysis of results.
12. Project: Assessment
- 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:
- Further information:
- Course may be repeated
- https://courses.fit.cvut.cz/NI-LSM/
- Time-table for winter semester 2023/2024:
- Time-table is not available yet
- Time-table for summer semester 2023/2024:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Bachelor program Informatics, unspecified branch, in Czech, 2015-2020 (elective course)
- Bachelor branch Security and Information Technology, in Czech, 2015-2020 (elective course)
- Bachelor branch Computer Science, in Czech, 2015-2020 (elective course)
- Bachelor branch Computer Engineering, in Czech, 2015-2020 (elective course)
- Bachelor branch Information Systems and Management, in Czech, 2015-2020 (elective course)
- Bachelor branch Web and Software Engineering, spec. Software Engineering, in Czech, 2015-2020 (elective course)
- Bachelor branch Web and Software Engineering, spec. Web Engineering, in Czech, 2015-2020 (elective course)
- Bachelor branch Web and Software Engineering, spec. Computer Graphics, in Czech, 2015-2020 (elective course)
- Master branch Knowledge Engineering, in Czech, 2016-2017 (elective course)
- Master branch Computer Security, in Czech, 2016-2019 (elective course)
- Master branch Computer Systems and Networks, in Czech, 2016-2019 (elective course)
- Master branch Design and Programming of Embedded Systems, in Czech, 2016-2019 (elective course)
- Master branch Web and Software Engineering, spec. Info. Systems and Management, in Czech, 2016-2019 (elective course)
- Master branch Web and Software Engineering, spec. Software Engineering, in Czech, 2016-2019 (elective course)
- Master branch Web and Software Engineering, spec. Web Engineering, in Czech, 2016-2019 (elective course)
- Master program Informatics, unspecified branch, in Czech, version 2016-2019 (elective course)
- Master branch System Programming, spec. System Programming, in Czech, 2016-2019 (elective course)
- Master branch System Programming, spec. Computer Science, in Czech, 2016-2017 (elective course)
- Master specialization Computer Science, in Czech, 2018-2019 (elective course)
- Bachelor branch Knowledge Engineering, in Czech, 2018-2020 (elective course)
- Master branch Knowledge Engineering, in Czech, 2018-2019 (elective course)
- Bachelor specialization Information Security, in Czech, 2021 (elective course)
- Bachelor specialization Management Informatics, in Czech, 2021 (elective course)
- Bachelor specialization Computer Graphics, in Czech, 2021 (elective course)
- Bachelor specialization Computer Engineering, in Czech, 2021 (elective course)
- Bachelor program, unspecified specialization, in Czech, 2021 (elective course)
- Bachelor specialization Web Engineering, in Czech, 2021 (elective course)
- Bachelor specialization Artificial Intelligence, in Czech, 2021 (elective course)
- Bachelor specialization Computer Science, in Czech, 2021 (elective course)
- Bachelor specialization Software Engineering, in Czech, 2021 (elective course)
- Bachelor specialization Computer Systems and Virtualization, in Czech, 2021 (elective course)
- Bachelor specialization Computer Networks and Internet, in Czech, 2021 (elective course)
- Study plan for Ukrainian refugees (elective course)
- Bachelor branch Web and Software Engineering, spec. Computer Graphics, in Czech, Dubin (elective course)