After successfully completing this course, students are able to:- formulate substantive questions in a suitable probabilistic framework- comprehend probabilistic approach(es) and inferential methods that can be used to answer these questions in a data-driven fashion- independently apply fundamental concepts of statistical learning to new data sets- select the appropriate methods depending on research question and data types- conduct independent analyses and formulate answers based on the outcomes

- lectures- homework problems- case studies

- recap of basic probability- discrete data models- Gaussian models- fundamental concepts of Bayesian and frequentist statistics- regression- advanced statistical learning methods in alignment with the students' interest

- fundamental linear algebra (matrix computations)- basic understanding of probability (including knowledge about important discrete and continuous distributions)- (some) prior exposition to statistical modeling

Murphy, Kevin P.: Machine learning : a probabilistic perspective (any edition)

- individual homework- case studies- final written exam in the last lecture with a possibility for a retake in early October if a student missed / failed the first attempt

contents of the lecture

correctness, level of comprehension, creativity to tackle new problems (if applicable)