Menu
Start
Research
Publications
Statistics playground
Teaching
CV
Contact


Lecture dates:
 Friday 18. April: no lecture (Easter)
 Friday 25. April 09.1510.45: Lecture 1 (Introduction)
 Friday 2. May 09.1510.45: Lecture 2 (Regression I)
 Friday 9. May: no lecture (room blocked)
 Friday 16. May 09.1510.45: Lecture 3 (Regression II)
 Friday 23. May 09.1510.45: Lecture 4 (Numerical Methods)
 Friday 30. May 09.1510.45: Lecture 5 (Model Comparison)
 Friday 6. June 09.1510.45: Lecture 6 (Classification)
 Friday 13. June 09.1510.45: Lecture 7 (Learning Theory)
 Friday 20. June 09.1510.45: backup date
Location:
Seminar room H310 in basement of House of Astronomy at Konigstuhl.
Requirements:
 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)
Syllabus
Problem sheets:
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 sidetopics 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 reversiblejump MCMC is an MCMC that can change the number of fit parameters during iterations. RJMCMC is described, e.g., on Wikipedia.
