Lecture: Foundations of Machine Learning and Data Mining

Examination date: 11.10.2006, 12.30- 14.00, Denickestrasse 15, Room 0506

Lab class homepage

Lecturers: Ralf Möller, Rainer Marrone

Combined lecture + lab class: Thursday 12.45 - 14.15, Location: ES42, Rm 2589

The lab class starts Fr. 21.4.06

Prerequisites:

Elementary knowledge in Computer Science and Mathematics as usual for a Master course.

Content (will be updated without notification):

• Introduction
• Uncertainty
• Wumpus example
• Bayesian Networks: syntax, semantics
• Bayesian Networks: construction and reasoning
• Learning from observations
  Inductive learning, learning decision trees, computational learning theory (PAC learning)
• Incremental learning
  Version spaces
• Knowledge in Learning
  Explanation-based learning, Learning using relevance information, Inductive Logic Programming
• Statistical Learning Methods
  • Parameter learning, learning Bayesian network structures, expectation maximization (EM algorithm)
  • Instance-based Learning, Neural Networks, Kernel Machines
  • Reinforcement Learning (not covered in SoSe 06)
• Data Mining, OLAP Processing (not covered in SoSe 06)

Acknowledgments:

Literature:

• Artificial Intelligence: A Modern Approach (Second Edition), Stuart Russel, Peter Norvig, Prentice Hall, 2003
  Chapters 13-14, 18-21.