13M054PO - Statistical Pattern Recognition

Course specification
Course title		Statistical Pattern Recognition
Acronym		13M054PO
Study programme		Electrical Engineering and Computing
Module		Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering
Type of study		master academic studies
Lecturer (for classes)		professor PhD Željko Đurović professor PhD Aleksandra Krstić
Lecturer/Associate (for practice)		Maša Tiosavljević, electrical and computer engineer
Lecturer/Associate (for OTC)		Maša Tiosavljević, electrical and computer engineer
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 extract and manipulate informative features, 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; Feature extraction and analysis; Hypothesis testing methods; Design of parametric classifiers; Design of nonparametric classifiers; Reduction dimension methods.
Contents of exercises		During the course students have to solve several practical problems: extract and analyze features from real signals, apply dimension reduction techniques, design of Bayes classifier and sequential test, design of linear and quadratic classifier.
Literature
Introduction to Statistical Pattern Recognition, Keinosuke Fukunaga, Academic Press, 1990 (Original title) Pattern Recognition, S. Theodoridis, K. Koutroumbas, Academic Press, 2009. (Original title) Introduction to Data Mining (2nd Edition), Pang-Ning Tan, Michael Steinbach, et. al, Pearson, 2018 (Original title) Statistical Pattern Recognition (3rd edition), A. Webb, K. Copsey, Wiley, 2011 (Original title)
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