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2024-02-12T22:10:46Z

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2023-03-03T21:46:08Z

Part 1

Wiki: Created page with " ===Part 1=== * Lecture 1: Introduction * Lecture 2: Naive Bayes classifier, expo..."

2023-03-03T13:25:50Z

Created page with " ===Part 1=== * Lecture 1: Introduction * Lecture 2: Naive Bayes classifier, expo..."

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===Part 1===
* Lecture 1: Introduction
* Lecture 2: Naive Bayes classifier, exponential family of distributions
** Exercise 1
* Lecture 3: Bayesian linear regression and Model evidence
* Lecture 4: Model evidence
** Test 1
* Lecture 5: Analysis of model evidence and statistical significance
** Task 2
** Practice 1
** Data for Practice 1
* Lecture 6: Bayesian logistic regression and feature selection, EM algorithm
** Task 3
* Lecture 7: EM-algorithm and variational EM-algorithm, missed data
* Lecture 8: Variational EM-algorithm
* Lecture 9: Gaussian processes and evolution of models in time
* Lecture 10: Construction of adequate multi-models
** Task 4
** Lecture 11: Monte Carlo Methods for Markov Chains
** Practice 1 (continued)
* Lecture 12: Hamiltonian Monte Carlo Methods for Markov Chains
* Lecture 13: Bayesian optimization.
** Written report

===Part 2===Choice of priors, non-informative priors, and Jeffreys distributions.
* Lecture 1 EM-algorithm
* Lecture 2 Applications of the EM algorithm
* Lecture 3 Variational EM-algorithm
* Lecture 3 Practice on EM and the variational EM algorithm
* Lecture 4 Hamiltonian Monte Carlo methods and comparison with the variational EM algorithm
* Lecture 4 Practice on the variational EM algorithm and comparison with HMC
* Lecture 5: Graphic models, Conditional independence of variables
** Practice 1
** Data for Practice 1
* Lecture 6: Oriented and undirected graphical models and the relationship between them
* Lecture 7: Factor graphs and exact inference in acyclic graphical models
* Lecture 8: Max-Sum Algorithm and Hidden Markov Models
* Lecture 9: Baum-Welch algorithm for estimating the parameters of hidden Markov models
* Lecture 9: Practice on the Baum-Welch algorithm
* Lecture 10: Algorithms for finding the minimum cut in graphs for output in graphical models
* Lecture 11: TRW algorithm for inference in cyclic graphical models for total energy
* Lecture 12: Estimation of hyperparameters of graphical models
** Competition
** Exam

===References===
# David MacKay, 2005, Information Theory, Inference, and Learning Algorithms
# Christopher Bishop, 2006, Pattern Recognition and Machine Learning
# David Barber, 2014, Bayesian Reasoning and Machine Learning
# Daphne Koller and Nir Friedman, 2009, Probabilistic Graphical Models
# Kevin P. Murphy, 2012, Machine Learning: a Probabilistic Perspective

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		+	\|description=This course is devoted to Bayesian model selection.
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Course syllabus: Bayesian model selection - Revision history

Vs at 22:10, 12 February 2024

Wiki: /* Part 1 */

Wiki: Created page with " ===Part 1=== * Lecture 1: Introduction * Lecture 2: Naive Bayes classifier, expo..."