Course Leader: Anders Grimvall
Phone: tel. +46-(0)13-28 14 82
E-mail: anders.grimvall@liu.se.se
Ph.D. candidates in statistics and other disciplines where knowledge of statistics is essential
Undergraduate courses in probability theory and statistical inference
The course aims to make provide the student with a solid background and understanding of statistical learning and modern regression techniques
Review of linear methods for prediction and classification
Non-linear methods for a single predictor
Model assessment and selection
Model inference
Specific methods for supervised learning and data mining
Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. New York :Springer
Active participation in classes
Oral presentations of selected chapters from the textbook
Computer exercises
Seminars approximately once a week.
Seminar 1: Linear methods for regression (Chapter 3)
Seminar 2: Linear methods for classification (Chapter 4)
Seminar 3: Smoothing splines and wavelet smoothing (Chapter 5)
Seminar 4: Kernel methods and local regression (Chapter 6)
Seminar 5: Information criteria, effective number of parameters, cross-validation (Chapter 7)
Seminar 6: Bootstrap, the EM algorithm, MCMC (Chapter 8)
Seminar 7: Generalized additive models and regression trees (Chapter 9)¨
Seminar 8: Boosting and multiple additive regression trees (Chapter 10)
Seminar 9: Projection persuit regression and neural networks (Chapter 11)
All seminars will take place in "Kompakta rummet", Department of mathematics, Building B, entrance 23.