Course detail

Bayesian Models for Machine Learning (in English)

FIT-BAYaAcad. year: 2020/2021

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

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.

Recommended optional programme components

Not applicable.

Literature

P Orbanz: Tutorials on Bayesian Nonparametrics: http://stat.columbia.edu/~porbanz/npb-tutorial.html (EN)

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.

Language of instruction

English

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MGMe , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, 5 credits, elective
    specialization NBIO , any year of study, winter semester, 5 credits, elective
    specialization NGRI , any year of study, winter semester, 5 credits, elective
    specialization NNET , any year of study, winter semester, 5 credits, elective
    specialization NVIZ , any year of study, winter semester, 5 credits, elective
    specialization NCPS , any year of study, winter semester, 5 credits, elective
    specialization NSEC , any year of study, winter semester, 5 credits, elective
    specialization NEMB , any year of study, winter semester, 5 credits, elective
    specialization NHPC , any year of study, winter semester, 5 credits, elective
    specialization NISD , any year of study, winter semester, 5 credits, elective
    specialization NIDE , any year of study, winter semester, 5 credits, elective
    specialization NISY , any year of study, winter semester, 5 credits, elective
    specialization NMAL , any year of study, winter semester, 5 credits, compulsory
    specialization NMAT , any year of study, winter semester, 5 credits, elective
    specialization NSEN , any year of study, winter semester, 5 credits, elective
    specialization NVER , any year of study, winter semester, 5 credits, elective
    specialization NSPE , any year of study, winter semester, 5 credits, elective

  • Programme IT-MGR-1H Master's

    branch MGH , any year of study, winter semester, 5 credits, recommended

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.

Syllabus

  1. Probability theory and probability distributions 
  2. Bayesian Inference (priors, uncertainty of the parameter estimates, posterior predictive probability) 
  3. Inference in Bayesian models with conjugate priors 
  4. Inference in Bayesian Networks (loopy belief propagation) 
  5. Expectation-Maximization algorithm (with application to Gaussian Mixture Model) 
  6. Approximate inference in Bayesian models using Gibbs sampling 
  7. Variational Bayes inference, Stochastic VB 
  8. Infinite mixture models, Dirichlet Process, Chinese Restaurant Process 
  9. Pitman-Yor Process for Language modeling 
  10. Expectation propagation 
  11. Gaussian Process 
  12. Auto-Encoding Variational Bayes 
  13. Practical applications of Bayesian inference

Fundamentals seminar

13 hours, compulsory

Teacher / Lecturer

Ing. Murali Karthick Baskar
Mireia Diez Sánchez, M.Sc., Ph.D.
Mgr. Lucas Antoine Francois Ondel, 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 hours, compulsory

Teacher / Lecturer

Ing. Murali Karthick Baskar
Mireia Diez Sánchez, M.Sc., Ph.D.
Mgr. Lucas Antoine Francois Ondel, 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.