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Course detail
FIT-BAYaAcad. year: 2023/2024
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, Practical applications of Bayesian inference
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Offered to foreign students
Entry knowledge
Rules for evaluation and completion of the course
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.
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. The course is taught in English.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
branch MGMe , any year of study, winter semester, compulsory-optional
specialization MGH , any year of study, winter semester, recommended
specialization NADE , any year of study, winter semester, electivespecialization NBIO , any year of study, winter semester, electivespecialization NGRI , any year of study, winter semester, electivespecialization NNET , any year of study, winter semester, electivespecialization NVIZ , any year of study, winter semester, electivespecialization NCPS , any year of study, winter semester, electivespecialization NSEC , any year of study, winter semester, electivespecialization NEMB , any year of study, winter semester, electivespecialization NEMB do 2021/22 , any year of study, winter semester, electivespecialization NHPC , any year of study, winter semester, electivespecialization NISD , any year of study, winter semester, electivespecialization NIDE , any year of study, winter semester, electivespecialization NISY do 2020/21 , any year of study, winter semester, electivespecialization NISY , any year of study, winter semester, electivespecialization NMAL , any year of study, winter semester, compulsoryspecialization NMAT , any year of study, winter semester, electivespecialization NSEN , any year of study, winter semester, electivespecialization NVER , any year of study, winter semester, electivespecialization NSPE , any year of study, winter semester, elective
Lecture
Teacher / Lecturer
Syllabus
Fundamentals seminar
Project