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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2021/2022

Bayesian Machine Learning

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Code Completion Credits Range
D01BSU ZK
Lecturer:
Václav Šmídl (guarantor)
Tutor:
Supervisor:
Department of Mathematics
Synopsis:
Requirements:
Syllabus of lectures:

1.Elementary use of Bayesian statistics.

2.Linear models for prediction, regularization, hierarchical priors.

3.Non-linear models for prediktion, nneural networks, estimation, regularization.

4.Gausian process for prediction, estimation of hyper-parameters, hierarchical Gausian processes.

5.Nelinear generative models, neural architectures of autoencoder type, regularization using Variational Bayes.

6.Dynamical models of sequences, parameter identification, recursive identification.

7.Multi-class classification, supervised and semisupervised learning.

8.Bayesian optimization, selection of stochastic process, hyperparameter selection, acquisition function.

Syllabus of tutorials:
Study Objective:
Study materials:

1.Ch. Bishop: Pattern Recognition and Machine Learning, Springer, 2006.

2.C. E. Rasmussen: Gaussian processes in machine learning, pages 63-71, Springer, Berlin, Heidelberg, 2004.

3.D. P. Kingma, M. Welling: Auto-encoding variational Bayes, arXiv preprint:1312.6114, 2013.

Note:
Time-table for winter semester 2021/2022:
Time-table is not available yet
Time-table for summer semester 2021/2022:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2022-08-14
For updated information see http://bilakniha.cvut.cz/en/predmet5715206.html