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Course, academic year 2017/2018
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Bayesian inference - NPFL108
English title: Bayesovská inference
Guaranteed by: Institute of Formal and Applied Linguistics (32-UFAL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/1 C(+Ex) [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: Czech, English
Teaching methods: full-time
Guarantor: Mgr. Ing. Filip Jurčíček, Ph.D.
Annotation -
Last update: Helena Kisvetrová (15.02.2013)

The course aims to provide students with basic understanding of modern Bayesian inference methods. The course will be composed of a series of lectures presented by experts from the Machine Learning Group, Cambridge University, UK. More information is available at
Literature -
Last update: JUDr. Dana Macharová (14.02.2013)

[1] D. Koller, N. Friedman: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series), The MIT Press, 2009, p. 1280

[2] C. M. Bishop, Pattern Recognition and Machine Learning, vol. 4, no. 4. Springer, 2006, p. 738.

Syllabus -
Last update: JUDr. Dana Macharová (14.02.2013)

Lecture topics:

Introduction to Bayesian Machine Learning: probabilistic models, inference, Bayes rule.

Approximate Inference: sampling methods, variational Bayes and expectation propagation.

Non-parametric Bayesian Methods: Gaussian processes and Dirichlet processes.

Bayesian Sparsity: spike and slab priors, dependency in sparsity enforcing priors, group sparsity.

Bayesian Latent Variable Methods: probabilistic matrix factorizations,

Bayesian mixtures of Gaussians.

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