- Friday 18. April: no lecture (Easter)
- Friday 25. April 09.15-10.45: Lecture 1 (Introduction)
- Friday 2. May 09.15-10.45: Lecture 2 (Regression I)
- Friday 9. May: no lecture (room blocked)
- Friday 16. May 09.15-10.45: Lecture 3 (Regression II)
- Friday 23. May 09.15-10.45: Lecture 4 (Numerical Methods)
- Friday 30. May 09.15-10.45: Lecture 5 (Model Comparison)
- Friday 6. June 09.15-10.45: Lecture 6 (Classification)
- Friday 13. June 09.15-10.45: Lecture 7 (Learning Theory)
- Friday 20. June 09.15-10.45: back-up date
Seminar room H-310 in basement of House of Astronomy at Konigstuhl.
- basic analysis (differentiation, integration)
- basic matrix algebra
- no prior knowledge of statistics required
- undergraduates (Bachelor/Master) need to solve exercises or take an (oral or written) exam to get the 1 credit for this course
- PhD students get the 1 credit for this course for attendance (documented by a list with signatures)
Literature and further material:
- Barlow 1999: Readible introduction but far too basic. Frequentist statistics, i.e., no big picture.
- Gelman, Carlin, Stern, Rubin 2004: Thorough introduction into Bayesian statistics.
- Hastie, Tibshirani, Friedman 2009: Very good/classic textbook. Also includes classification.
- MacKay 2003: A classic and well written. Sometimes a bit philosophical and takes several detours on side-topics that are intersting but distract from the red line. The book is freely available here.
- Vapnik 1999: Insightful and illuminating read written by one of the founders of Learning Theory, though later chapters become increasingly mathematical.
- Youtube has many nice lecture courses on data analysis, e.g.:
- Paper on Bayes factors by Kass and Raftery. Sections 1, 3 and in particular the discussion in Section 8 are very interesting.
- The reversible-jump MCMC is an MCMC that can change the number of fit parameters during iterations. RJMCMC is described, e.g., on Wikipedia.