Difference between revisions of "Methodologies for Intelligent Systems"

Revision as of 11:20, 16 June 2009

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.

Teachers

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

• Matteo Matteucci: the course teacher
• Davide Eynard: the teaching assistant on clustering
• Rossella Blatt: the teaching assistant on feature selection/projection

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.

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

• Lecture 1: Introduction to Machine Learning
• Lecture 2: Probability for Dataminers
• Lecture 3: Decision Trees
• Lecture 4: Decision Rules
• Lecture 5: Bayesian Classifiers
• Lecture 6: Bayesian Networks
• Lecture 7: Markov Chains and Hidden Markov Models

Clustering

• Clustering Lecture 1: Introduction (03/04/2008)
• Clustering Lecture 2: K-Means and Hierarchical (03/04/2008)
• Clustering Lecture 3: Fuzzy, Gaussians, and SOM (03/04/2008)
• Clustering Lecture 4: Vector Spacec Model and PDDP
• Clustering Lecture 5: DBSCAN and Jarvis Patrick
• Clustering Lecture 6: Evaluation measures

Dimensionality Reduction and Feature Selection

• Dimensionality Reduction Lecture 1: Dimensionality reduction Intro
• Dimensionality Reduction Lecture 2: Feature extraction, PCA and LDA
• Dimensionality Reduction Lecture 3: Feature selection
• Genetic Algorithms: a rather comprehensive tutorial
• Algorithm Evaluation: from cross-validation to confidence intervals

Additional Lecture Notes and Bibliography

• Fundamental Problems for HMM: a document to introduce Hidden Markov Models and the three fundamental questions about them.
• An exercise on modeling and reasoning with Bayesian Networks.

Homeworks !

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 3rd of July!

• Homework for the academic year 2008/2009

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

• Homework for the academic year 2007/2008
• Homework for the academic year 2006/2007
• Homework for the academic year 2005/2006