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FIT-BAYaAcad. year: 2024/2025
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
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Offered to foreign students
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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.
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Classification of course in study plans
specialization MGH , 0 year of study, winter semester, recommended course
specialization NGRI , 0 year of study, winter semester, electivespecialization NADE , 0 year of study, winter semester, electivespecialization NISD , 0 year of study, winter semester, electivespecialization NMAT , 0 year of study, winter semester, electivespecialization NSEC , 0 year of study, winter semester, electivespecialization NISY up to 2020/21 , 0 year of study, winter semester, electivespecialization NNET , 0 year of study, winter semester, electivespecialization NMAL , 0 year of study, winter semester, compulsoryspecialization NCPS , 0 year of study, winter semester, electivespecialization NHPC , 0 year of study, winter semester, electivespecialization NVER , 0 year of study, winter semester, electivespecialization NIDE , 0 year of study, winter semester, electivespecialization NISY , 0 year of study, winter semester, electivespecialization NEMB , 0 year of study, winter semester, electivespecialization NSPE , 0 year of study, winter semester, electivespecialization NEMB , 0 year of study, winter semester, electivespecialization NBIO , 0 year of study, winter semester, electivespecialization NSEN , 0 year of study, winter semester, electivespecialization NVIZ , 0 year of study, winter semester, elective
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