Statistical data analysis 2

General data

Course ID:	1000-2M13SAD2
Erasmus code / ISCED:	11.303 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Statistical data analysis 2
Name in Polish:	Statystyczna analiza danych 2 (wspólne z 1000-718SAD)
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Elective courses for Computer Science
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Prerequisites (description):	R programming, passed statistical data Analysis 1, English (advanced)
Short description:	Advanced course in machine learning methods.
Full description:	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 <odels, as examples of bayesian networks, parameter estimation and inference 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) optionally also includes 9. variational inference. 10. exploratory data analysis on example of single cell RNA seq data
Bibliography:	Pattern Recognition and Machine Learning, C. Bishop Probabilistic Modeling in Bioinformatics and Medical Informatics, D. Husmeier, R. Dybowski and S, Roberts
Learning outcomes:	Machine learning and statistical inference, focused on probabilistic graphical models
Assessment methods and 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 egzam: 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.

This course is not currently offered.

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