Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
The Study plan 2008/2009 will be available by May 2nd. While waiting for it to be published, consult the Study plan for the current academic year at the following link.
Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Machine Learning & Pattern Recognition (2009/2010)
Teaching code
4S02803
Teacher
Coordinator
Credits
6
Also offered in courses:
- Pattern Recognition of the course Masters in Intelligent and Multimedia Systems
Language
Italian
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
1st Semester dal Oct 1, 2009 al Jan 31, 2010.
Learning outcomes
The class aims at providing the basic theories and the most significant methods related to the analysis of data of whatever nature, that is theory and methods related to pattern recognition and machine learning.
This discipline is at the base and complete many other disciplines, recently of larger diffusion, like image processing, the analysis of huge quantities of data, artificial intelligence, databases, and many others.
In tha class, special emphasis will be devoted to the probabilistic and statistical techniques, in particular to the learning of systems for classification and recognition.
Many applications are involved by this discipline.
To quote some, image analysis and computer vision, data mining, bioinformatics, biomedical image and biological data analysis and interpretation (e.g., genomics, proteomics, etc.), biometry, video surveillance, robotics, speech recognition, and many others.
Program
* Introduction: what it is, what is useful for, systems, applications
* Recognition and classification
* Bayes theory
* Parameters' estimation
* Non parametric methods
* Linear classifiers, non linear classifiers, discriminant functions
* Feature estraction and selection, PCA and Fisher transform
* Expectation-Maximization e mixture of Gaussians
* Generative and discriminative methods
* Kernel methods e Support Vector Machines
* Artificial Neural Networks
* Hidden Markov Models
* Unsupervised classification (clustering)
In total, there are 32 hours of Theory lectures and 24 hours of laboratory.
Examination Methods
An oral interview with 2 questions, aimed at verifying the understanding of theoretical concepts, and a project aimed at understanding the mastering of the mathematical and computer tools.
The oral test can be substituted with a written test with short questions similar to the oral one.
