Course detail

Bayesian Models for Machine Learning (in English)

FIT-BAYaAcad. year: 2021/2022

Probability theory and probability distributions, Bayesian Inference, Inference in Bayesian models with conjugate priors, Inference in Bayesian Networks, Expectation-Maximization algorithm, Approximate inference in Bayesian models using Gibbs sampling, Variational Bayes inference, Stochastic VB, Infinite mixture models, Dirichlet Process, Chinese Restaurant Process, Pitman-Yor Process for Language modeling, Expectation propagation, Gaussian Process, Auto-Encoding Variational Bayes, Practical applications of Bayesian inference

Language of instruction

English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Lukáš Burget, Ph.D.

Department

Department of Computer Graphics and Multimedia (UPGM)

Offered to foreign students

Of all faculties

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Half-semestral exam (24pts)
Submission and presentation of project (25pts)
Semestral exam, 51pts.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

To demonstrate the limitations of Deep Neural Nets (DNN) that have become a very popular machine learning tool successful in many areas, but that excel only when sufficient amount of well annotated training data is available. To present Bayesian models (BMs) allowing to make robust decisions even in cases of scarce training data as they take into account the uncertainty in the model parameter estimates. To introduce the concept of latent variables making BMs modular (i.e. more complex models can be built out of simpler ones) and well suitable for cases with missing data (e.g. unsupervised learning when annotations are missing). To introduce basic skills and intuitions about the BMs and to develop more advanced topics such as: approximate inference methods necessary for more complex models, infinite mixture models based on non-parametric BMs, or Auto-Encoding Variational Bayes. The course is taught in English.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Type of course unit

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.

Syllabus

Probability theory and probability distributions
Inference in Bayesian Networks
Markov Random Fields, Factor graphs, Belief Propagation
Expectation-Maximization algorithm (with application to Gaussian Mixture Model)
Bayesian Inference (priors, uncertainty of the parameter estimates, posterior predictive probability)
Inference in Bayesian models with conjugate priors
Variational Bayes inference, Mean-field approximation
Approximate inference in Bayesian models using Gibbs sampling
Collapsed Gibbs sampling based inference for Bayesian Gaussian Mixture Model
Infinite mixture models, Dirichlet Process, Chinese Restaurant Process
Pitman-Yor Process for Language modeling

Fundamentals seminar

13 hod., compulsory

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.
Mireia Diez Sánchez, M.Sc., Ph.D.

Syllabus

Lectures will be immediately followed by demonstration exercises where examples in Python will be presented. Code and data of all demonstrations will be made available to the students and will constitute the basis for the project.

Project

13 hod., compulsory

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.

Syllabus

The project will follow on the demonstration exercises and will make the student work on provided (simulated or real) data. The students will work in teams in "evaluation" mode and present their results at the final lecture/exercise.

VUT

Faculties

University Institutes

Parts

Bayesian Models for Machine Learning (in English)

Type of course unit