# Data mining and statistical learning, 15 credits

**Course Leader:** Anders Grimvall

**Phone:** tel. +46-(0)13-28 14 82

**E-mail:** anders.grimvall@liu.se.se

### Target group

Ph.D. candidates in statistics and other disciplines where knowledge of statistics is essential

### Prerequisites

Undergraduate courses in probability theory and statistical inference

### Objectives

The course aims to make provide the student with a solid background and understanding of statistical learning and modern regression techniques

### Contents

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

### Textbook

Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. New York :Springer

### Examination

Active participation in classes

Oral presentations of selected chapters from the textbook

Computer exercises

### Schedule

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.