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

- 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 (20% of the final grade)- two written exams (midterm and final, 40% of the final grade each). There is the possibility for a retake in late September / early October (accounting for 40% of the final grade, covering the entire material), in order to make up for exactly one of the two previous exams.Overall scores above 50% guarantee a passing grade.In this course, you will receive a grade if and only if you have participated actively in at least one grade-relevant activity (e.g., you ticked a problem in an exercise class, you participated in a graded quiz, you attempted an exam, etc.).

contents of the course

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