Lecture: Foundations of Machine Learning and Data Mining

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Lecturers: Ralf Möller, Rainer Marrone

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

Prerequisites:

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

Content (will be updated without notification):

• Introduction
• Learning from observations
  Inductive learning, introduction to learning decision trees
• Decision tree learning
  Information theory, information gain (ID3), extensions (C4.5), translating decision trees to rules
• Computational learning theory (PAC learning), incremental learning (version spaces)
• Uncertainty
• Bayesian networks
• Learning parameters of Bayesian networks
  BME, MAP, ML, EM algorithm
• Learning structures of Bayesian networks
• kNN-Classifier, neural network classifier, support vector machine (SVM) classifier
• Clustering
  Distance measures, k-means clustering, nearest neighbor clustering
• Knowledge in learning
  Inductive logic programming
• Learning of probabilistic relational models (PRMs)

Acknowledgments:

Literature:

• Artificial Intelligence: A Modern Approach (Second Edition), Stuart Russel, Peter Norvig, Prentice Hall, 2003
  Chapters 13-14, 18-21.
• Introducion to Machine Learning Ethem Alpaydin, MIT Press, 2004

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