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13M051SKS - Statistical Signal Classification

Course specification
Course title Statistical Signal Classification
Acronym 13M051SKS
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study master 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 signal classification: hypothesis testing, parametric and nonparametric classification.
    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).
    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; Signal classification 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. Signal classification based on fuzzy logic.
    Literature
    1. Introduction to Statistical Pattern Recognition, Keinosuke Fukunaga, Academic Press, 1990.
    2. Pattern Recognition, S. Theodoridis, K. Koutroumbas, Academic Press, 2009.
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 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