Bayesian Machine Learning

Course guarantor:

Václav Šmídl

Lecturer:

Václav Šmídl

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 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: