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
7. model selection, model evidence, learning model structure, tree models, general models, structural EM
8. Sampling (MCMC, Gibbs sampling)
9. variational inference.
Type of course
Prerequisites (description)
Course coordinators
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)
15% computational project 1
15% computational project 2
Mid-term test 15%
5% lab activity
Required to pass: 50%
Zero exam: oral, the date is agreed individually, no later than a week before the final exam.
Criteria for admission to the zero exam: 45 points for projects and test.
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:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: