13M051SKS - Statistical Signal Classification

Course specification
Course title Statistical Signal Classification
Acronym 13M051SKS
Study programme Electrical Engineering and Computing
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 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).
    URL to the subject page
    URL to lectures
    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.
    1. Introduction to Statistical Pattern Recognition, Keinosuke Fukunaga, Academic Press, 1990 (Original title)
    2. Pattern Recognition, S. Theodoridis, K. Koutroumbas, Academic Press, 2009. (Original title)
    3. Introduction to Data Mining (2nd Edition), Pang-Ning Tan, Michael Steinbach, et. al, Pearson, 2018 (Original title)
    4. 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
    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
    Colloquia 0
    Seminars 0