Difference between revisions of "Pattern Analysis and Machine Intelligence"

Revision as of 01:27, 28 May 2013

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

12/04/2011: detailed schedule modified; no lecture on 18/5 and additional lectures on 31/05 and 01/06 (I forgot them originally)
14/03/2011: added the detailed schedule. NOTE that tomorrow class starts at 14:15 instead of 13:15
08/03/2011: the course started 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.

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
• Luigi Malago': the teaching assistant on regression
• Davide Eynard: the teaching assistant on clustering

Course Program

Techniques from machine and statistical learning are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course follows, at least partially, the outline of The Elements of Statistical Learning book (by Trevor Hastie, Robert Tibshirani, and Jerome Friedman):

• 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. ).

Detailed course schedule

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all Tuesdays and Wednesdays are in!!

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:

• Course introduction: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised learning and two algorithms for classification (taken from The Elements of Statistical Learning book).
• Linear Classification Examples: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from The Elements of Statistical Learning book).
• Least Angle Regression http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf
• ...

Clustering

• Lesson 1: Introduction to Clustering and K-Means (slides, handouts)

• Lesson 2: K-Means alternatives, Hierarchical, SOM (slides, handouts)

• Lesson 3: Mixture of Gaussians, DBSCAN, Jarvis-Patrick (slides, handouts)

• Lesson 4: Evaluation measures (slides, handouts)

Lectures will be based on material taken from the aforementioned slides and from the following book.

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction. by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.