Methodologies for Intelligent Systems

From Chrome
Revision as of 19:07, 7 August 2010 by Matteo (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

The following are last minute news you should be aware of ;-)

07/08/2010: Summer grades are out details are here!
23/07/2010: the exam of 26/07/2010 will be in room 1.4 starting at 9:00 am
29/06/2010: homework EXTENSION, new deadline 10/07/2010
16/06/2010: the homework is out! Turn it in by 05/07/2010 !!!
09/03/2010: the course starts today!

Course Aim & Organization

The objective of this course is to give an advanced presentation of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling.


The course is composed by a blending of lectures and exercises by the course teacher and some teaching assistants.

Course Program and Schedule

These techniques are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, their implementation, and applications.The course is composed by a set of selfcontained lectures on specific techniques such as decision trees, decision rules, Bayesian networks, clustering, etc. Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:

  • Machine Learning and Pattern Classification: in this part of the course the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistics and information theory;
  • Unsupervised Learning Techniques: the most common approaches to unsupervised learning are described mostly focusing on clustering techniques, rule induction, Bayesian networks and density estimators using mixure models;
  • Supervised Learning Techniques: in this part of the course the most common techniques for Supervised Learning are described: decision trees, decision rules, Bayesian classifiers, hidden markov models, lazy learners, etc.
  • Feature Selection and Reduction: techniques for data rediction and feature selection will be presented with theory and applications
  • Model Validation and Selection: model validation and selection are orthogonal issues to previous technique; during the course the fundamentals are described and discussed (e.g., AIC, BIC, cross-validation, etc. ).

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are corret "up to some last minute change".

Course Evaluation

The course evaluation is composed by two parts:

  • A homework with exercises covering the whole program that counts for 30% of the course grade
  • A oral examination covering the whole progran that count for 70% of the course grade

The homework is just one per year, it will be published at the end of the course and you will have 15 days to turn it in. It is not mandatory, however if you do not turn it in you loose 30% of the course grade. There is the option of substitute the homework with a practical project, but this has to be discussed and agreed with the course professor.

Teaching Material

In the following you can find the lecture slides used by the teacher and the teaching assistants during classes. Some additional material that could be used to prepare the oral examination is provided as well together with the homework.

Machine Learning and Pattern Recognition


Dimensionality Reduction and Feature Selection


The homework, although not mandatory, counts for the 30% of the course grade (i.e., if you do not turn it in you loose 30% of the final grade). You have 15 days to turn it in to the teacher. This year the homework is due by the 5th of July! Please turn in a digital (or digitalized) copy of your homework.

Past years course homework; you can use them to make some practice and prepare this year homework ;-)

Additional Lecture Notes and Bibliography