Course detail
Bayesian Models for Machine Learning (in English)
FIT-BAYaAcad. year: 2022/2023
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
Number of ECTS credits
Mode of study
Guarantor
Offered to foreign students
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
-
Mid-term exam (24 points)
-
Submission and presentation of project (25 points)
-
Final exam (51points)
To get points from the exam, you need to get min. 20 points, otherwise the exam is rated 0 points.
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
C. Bishop: Pattern Recognition and Machine Learning, Springer, 2006 (EN)
P Orbanz: Tutorials on Bayesian Nonparametrics: http://stat.columbia.edu/~porbanz/npb-tutorial.html (EN)
S. J. Gershman and D.M. Blei: A tutorial on Bayesian nonparametric models, Journal of Mathematical Psychology, 2012. (EN)
Classification of course in study plans
- Programme IT-MGR-1H Master's
branch MGH , 1 year of study, winter semester, recommended course
- Programme IT-MSC-2 Master's
branch MGMe , 0 year of study, winter semester, compulsory-optional
- Programme MIT-EN Master's 0 year of study, winter semester, compulsory-optional
- Programme MITAI Master's
specialization NADE , 0 year of study, winter semester, elective
specialization NBIO , 0 year of study, winter semester, elective
specialization NCPS , 0 year of study, winter semester, elective
specialization NEMB , 0 year of study, winter semester, elective
specialization NGRI , 0 year of study, winter semester, elective
specialization NHPC , 0 year of study, winter semester, elective
specialization NIDE , 0 year of study, winter semester, elective
specialization NISD , 0 year of study, winter semester, elective
specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
specialization NMAL , 0 year of study, winter semester, compulsory
specialization NMAT , 0 year of study, winter semester, elective
specialization NNET , 0 year of study, winter semester, elective
specialization NSEC , 0 year of study, winter semester, elective
specialization NSEN , 0 year of study, winter semester, elective
specialization NSPE , 0 year of study, winter semester, elective
specialization NVER , 0 year of study, winter semester, elective
specialization NVIZ , 0 year of study, winter semester, elective
specialization NISY , 0 year of study, winter semester, elective
specialization NEMB up to 2021/22 , 0 year of study, winter semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Probability theory and probability distributions
- Bayesian Inference (priors, uncertainty of the parameter estimates, posterior predictive probability)
- Inference in Bayesian models with conjugate priors
- Inference in Bayesian Networks (loopy belief propagation)
- Expectation-Maximization algorithm (with application to Gaussian Mixture Model)
- 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
Fundamentals seminar
Teacher / Lecturer
Syllabus
Project
Teacher / Lecturer
Syllabus