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
- Recognition of objects
- Recognition of scenes
- Tracking
- Analysis of social networks
- Video surveillance
- Recommender systems

Examination Methods

Project and oral.

Teaching materials e documents

•   CAP 0 - Introduzione al corso   (vnd.ms-powerpoint, it, 169 KB, 10/15/12)
•   CAP 1 - Introduzione alla pattern recognition   (vnd.ms-powerpoint, it, 5660 KB, 10/15/12)
•   CAP 2 -Teoria di Bayes - approfondimento   (vnd.ms-powerpoint, it, 1004 KB, 10/15/12)
•   CAP 3 - Estrazione delle features: PCA   (vnd.ms-powerpoint, it, 1278 KB, 10/21/12)
•   CAP 3 part 2 - Estrazione delle features: LDA   (pdf, it, 810 KB, 10/21/12)
•   CAP 3 part 3 - Estrazione delle features per object recognition: Bag of Words   (vnd.ms-powerpoint, it, 1938 KB, 10/29/12)
•   CAP 4 - Training: stima parametrica   (octet-stream, it, 3636 KB, 1/14/13)
•   CAP 5 - Training: stima non parametrica   (vnd.ms-powerpoint, it, 3191 KB, 11/18/12)
•   CAP 6 - Classificatori generativi   (vnd.ms-powerpoint, it, 4722 KB, 12/3/12)
•   CAP 7 - Clustering   (zip, it, 6575 KB, 1/6/13)
•   LAB 0 -Ripasso MATLAB   (zip, it, 0 KB, 10/21/12)
•   LAB 1 -Classificatori di Bayes   (zip, it, 6398 KB, 10/21/12)
•   LAB 2 - PCA   (zip, it, 5057 KB, 11/14/12)
•   Lab 3 - Tracking   (zip, it, 2 KB, 12/5/12)
•   Lab 4 - HMM   (zip, it, 6930 KB, 12/19/12)
•   Lab 5 - Clustering   (zip, it, 5653 KB, 1/9/13)
•   Progetti per esame   (octet-stream, it, 3048 KB, 12/16/12)