## Literature

The required literature consists of the lecture slides (see the schedule), the lecture notes, and selected book chapters and articles. Below, we specify the required literature per subject.

#### Introduction

- A. Feelders, H. Daniels, M. Holsheimer Methodological and Practical Aspects of Data Mining Information & Management 37(5), 2000, pp. 271-281.

#### Classification Trees, Regression Trees, Bagging and Random Forests

- Lecture Notes on Classification Trees.
- Chapter 8 from An Introduction to Statistical Learning (ISLR), except for section 8.2.3 (Boosting). Look here for slides and video lectures for the book.

#### Undirected Graphical Models (Markov Random Fields)

#### Frequent Pattern Mining

#### Text Mining

- Chapters 6 (Naive Bayes and Sentiment Classification) and 7 (Logistic Regression) from Speech and Language Processing (3rd ed.) by Jurafsky and Martin.

#### Bayesian Networks

#### Social Network Mining

- Qing Lu, Lise Getoor Link-based Classification Proceedings of ICML-2003, Washington DC, 2003.