Lecturer: Rainer Marrone
Lecture: 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
Extensions (C4.5), translating decision trees to rules - 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)
Additional Resources:
- Weka download
- Decision Tree Tool
- Top 10 algorithms in Data Mining
-
Java Code for AIMA topics. Taken from the AIMA site.
Acknowledgments:
Slides were taken from courses by Stuart Russell, Hwee Tou Ng, Reijer Grimbergenm, Cristina Conati, and Jean Claude LatombeLiterature:
- 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
Previous exams
