Teaching code

4S02803

Teacher

Marco Cristani

Coordinatore

Marco Cristani

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

I sem. dal Oct 1, 2014 al Jan 30, 2015.

Elearning Seminars

Learning outcomes

Pattern Recognition is a highly pervasive discipline, both for science and industry. It focuses on the creation of classifiers, that is, algorithms able to learn aspects of the reality that surrounds us and to make appropriate decisions when in the presence of new stimuli. Speech recognition, automotive, surveillance systems, quality control systems, recommender systems, search engines, social networks, interactive tools (Kinect, Wii) are just some of the many applications that rely on the presence of classifiers. The Pattern Recognition course is intended to provide the methodological principles at the basis of the classification, together with the most modern techniques that can solve problems until a few years ago unmanageable. In other words, the course aims to be the best compromise between theory and practice, making the student can solve problems with tangible and important techniques from solid theoretical point of view.

Program

The course can be divided into two parts, the methodology and the application, which go hand in hand during the course.

Methodologies
- Introduction
- Recognition and classification
- Bayesian Decision Theory
- Parameters Estimation
- Nonparametric Methods of Parameters Estimation
- Linear and non-linear discriminant functions
- Extraction and feature selection, PCA, Fisher transform
- Expectation-Maximization Algorithm on mixtures of Gaussians
- Generative and discriminative methods
- Kernel Methods and Support Vector Machines
- Hidden Markov Models
- Methods for unsupervised classification (clustering)
- Pattern recognition for the analysis and recognition in images and videos

Applications
- Face recognition
- Tracking
- Video surveillance

Textbooks:
- Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Examination Methods

Project and oral exam.

Bibliography

Teaching materials

•   Lab.0 Ripasso   (zip, it, 0 KB, 13/10/14)
•   Lab.1 Classificatori di Bayes applicati alle immagini   (zip, it, 6504 KB, 23/10/14)
•   Lab.2 Classificatori di Bayes applicati alle immagini e ai filmati   (zip, it, 1 KB, 30/10/14)
•   Lab.3 PCA - rudimenti   (zip, it, 1 KB, 06/11/14)
•   Lab.4 PCA - eigenfaces   (zip, it, 5045 KB, 10/11/14)
•   Lab. 5 Fisher Discriminant Analysis   (octet-stream, it, 2 KB, 20/11/14)
•   Lab.6 EM   (zip, it, 5064 KB, 11/12/14)
•   Lab.7 Tracking   (zip, it, 2 KB, 18/12/14)
•   Lab.8 HMM   (zip, it, 6930 KB, 15/01/15)
•   Lez.0 Presentazione del docente   (zip, it, 341 KB, 13/10/14)
•   Lez.1 Introduzione al corso   (zip, it, 15263 KB, 13/10/14)
•   Lez.2 Classificazione di Bayes   (zip, it, 1181 KB, 20/10/14)
•   Lez.3 Estrazione delle features - Parte 1   (zip, it, 5067 KB, 27/10/14)
•   Lez.3 Estrazione delle features - Parte 2   (pdf, it, 810 KB, 20/11/14)
•   Lez.4 Stima non parametrica dei classificatori   (zip, it, 5737 KB, 15/12/14)
•   Lez.4 Stima parametrica dei classificatori   (zip, it, 8225 KB, 01/12/14)
•   Lez.6 Classificatori Generativi   (zip, it, 7389 KB, 12/01/15)