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13E054PO - Pattern Recognition

Course specification
Course title Pattern Recognition
Acronym 13E054PO
Study programme Electrical Engineering and Computing
Module
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition none
The goal Objective of the course is for the students to be informed about the statistical methods for pattern recognition: hypothesis testing, parametric and nonparametric classification, clustering.
The outcome Learning outcomes of the course are following: students´ ability to generate or to collect high quality and informative training sets of data, to apply appropriate statistical pattern recognition technique (hypothesis testing, parametric or nonparametric classifier), to design system for data clustering.
Contents
Contents of lectures Overview of random variables and vectors; Important results from linear algebra ; Hypothesis testing methods; Design of parametric classifiers; Design of nonparametric classifiers; Reduction dimension methods; Clustering techniques; Pattern recognition based on fuzzy logic and neural networks.
Contents of exercises During the course students have to solve three practical problems: 1. Design of Bayes classifier and sequential test; 2. Design of linear and quadratic classifier; 3. Clustering of data.
Literature
  1. Introduction to Statistical Pattern Recognition, Keinosuke Fukunaga, Prentice Hall, 1992.
  2. Script of pattern recognition (electronic form), Zeljko Djurovic, www.automatika.etf.bg.ac.yu
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
3 1 1
Methods of teaching 3x15 hours of lectures, 1x15 hours of practical exercising with computers
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 70
Practical lessons 30 Oral examination 0
Projects
Colloquia 0
Seminars 0