MS1SKS - Statistical Signal Classification
Course specification | ||||
---|---|---|---|---|
Course title | Statistical Signal Classification | |||
Acronym | MS1SKS | |||
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 | ||||
| ||||
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 | 0 | |||
Colloquia | 0 | |||
Seminars | 0 |