2010/2011 lectures will be given in the second semester (March-June 2011).
Information about the 2010/2011 class will be given in these pages later.
Information about January February exams are in the Exams section of these web pages.

28 September - 23 December 2009

Monday 15:40 - 19:00
Wednesday 17:30 -19:00

DIS Via Ariosto 25 - Room A3

Tuesday, 10-12, DIS Via Ariosto 25 - Room B111

The objectives of this course are to present a wide spectrum of Machine Learning methods and algorithms, discuss their properties, convergence criteria and applicability. The course will also present many examples of successful application of Machine Learning algorithms in different application scenarios.

The main outcome of the course is the capability of the students of solving a learning problem, by a proper formulation of the problem, a proper choice of the algorithm suitable to solve the problem and the execution of experimental analysis to evaluate the results obtained.

1. Introduction to Machine Learning
2. Inductive Learning
3. Decision Trees
4. Evaluation of hypotheses.
5. Classification with linear models
6. Bayesian Learning
7. Unsupervised Learning and clustering
8. Support Vector Machines
9. Neural networks
10. Genetic algorithms
11. Reinforcement Learning
12. Boosting
13. Multi-agent learning
14. Data discovery e data mining
15. Robot Learning