Program Contents

Data Mining
Foundations. Classification. Regression and Numerical Prediction. Multiple Models for Classification and Regression. Frequent Patterns. Meta Learning. Statistical Relational Learning.

Logic Programming
Prolog and the Warren Abstract Machine. Parallelism in Logic Programming. Tabling in Logic Programming.

Inductive Logic Programming
Foundations. Inverse Resolution. Mode Directed Inverse Entailment. Logical Decision Trees. Stochastic-Based Search Algorithms. Applications.


Machine Learning
Tom M. Mitchell. McGraw Hill, 1997.

Intelligent Data Analysis: an Introduction
David J. Hand. Springer-Verlag, 1999.

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
Ian Witten, Eibe Frank. Morgan Kaufmann, 2000.

Principles of Data Mining (Adaptive Computation and Machine Learning)
David J. Hand, et al. The MIT Press, 2001.

Relational Data Mining
S. Dzeroski, N. Lavrac. Springer-Verlag, 2001.

Warren's Abstract Machine - A Tutorial Reconstruction
H. At-Kaci. MIT Press, 1991.

An Abstract Machine for Tabled Execution of Fixed-Order Stratified Logic Programs
K. Sagonas, T. Swift. ACM Transactions on Programming Languages and Systems, volume 20(3), pages 586-634, 1998.

Efficient Access Mechanisms for Tabled Logic Programs
I. V. Ramakrishnan, et al. Journal of Logic Programming, volume 38(1), pages 31-54, 1999.

Parallel Execution of Prolog Programs: A Survey
G. Gupta, et al. ACM Transactions on Programming Languages and Systems, volume 23(4), pages 472-602, 2001.

Inductive Logic Programming: Techniques and Applications
N. Lavrac, S. Dzeroski. Ellis Horwood, New York, 1994.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
T. Hastie, et al. Springer Texts in Statistics. 2001.

An Introduction to Statistical Relational Learning
Lise Getoor, Ben Taskar. MIT Press, 2007.

The students will also be expected to read and discuss papers in the major conferences and journals in the area, such as: ECML, ICDM, ICLP, ICML, JMLR, KDD, MLJ, PKDD, TPLP.