This course will give a detailed overview of statistical models for modern regression and classification with emphasis on applications. A number of examples and case studies will be examined. This course will cover a range of models from linear regression through various classes of more flexible models including fully nonparametric regression models. We will consider both regression and classification problems. Methods such as splines, additive models, multivariate adaptive regression splines (MARS), neural networks, classification and regression trees (CART), linear and flexible discriminant analysis, generalized additive models, nearest- neighbor rules and learning vector quantization will be discussed.

The evaluation will be based on assessments of two project assignments applying the various methods to analyze given datasets plus a review of an interesting article that used any of the methods.

Textbooks: 

The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition). Trevor Hastie, Robert Tibshirani and Jerome Friedman.

Applied Linear Regression Models, Fourth Edition by Kutner, Nachtsheim and Neter.

Recommended preceding studies:

Basic courses of statistics and Regression analysis.

Please note

Students who have completed course MTTA2 Ei-parametrinen regressio can not get full credits of this course because some of the contents overlap.