Statistical data analysis 2 1000-718SAD

Syllabus
1. frequentist vs bayesian approach in statistical modeling
2. bayesian networks (probabilistic graphical models)
3. parameter inference in probabilistic graphical models with fully observed data
4. EM algorithm (parameter estimation in models with hidden variables)
5. Markov chains and Hidden Markov 6. Exact inference in graphical models (factor graphs, the sum product algorithm, Cluster trees, potentials, Message passing, Junction tree algorithm)
7. model selection, model evidence, learning model structure, tree models, general models, structural EM
8. Sampling (MCMC, Gibbs sampling)
9. variational inference.

Course coordinators

Term 2025Z:

Anna Gambin

Term 2024Z:

Anna Gambin

Term 2026Z:

Damian Wójtowicz

Type of course

obligatory courses

Requirements

Linear algebra
Statistical data analysis

Prerequisites (description)

Python programming, passed Statistical Data Analysis 1, English (advanced)

Learning outcomes

Machine learning and statistical inference, focused on probabilistic graphical models

Assessment criteria

Rules for passing the course (scoring):
50% exam at the end (a test)
25% computational project
20% mid-term test
5% lab activity
Required to pass: 50%

Bibliography

Pattern Recognition and Machine Learning, C. Bishop

Probabilistic Modeling in Bioinformatics and Medical Informatics, D. Husmeier, R. Dybowski and S, Roberts

Additional information

Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes: