13E054PO - Pattern Recognition
Course specification | ||||
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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 | ||||
URL to the subject page | https://automatika.etf.bg.ac.rs/sr/13e054po | |||
URL to lectures | https://teams.microsoft.com/l/team/19%3ADAf8gJWn34eHzHu51Y5Fw9g0bJL4l918CzzrrFfcLQ01%40thread.tacv2/conversations?groupId=fb07930a-c0ed-4993-9f46-3454334fa027&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba | |||
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 four practical problems: 1. Design of Bayes classifier and sequential test; 2. Design an algorithm for handwritten digit recognition; 3. Design of linear and quadratic classifier; 4. Clustering of data. | |||
Literature | ||||
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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 auditory exercises and 1x15 hours of computers exercises | |||
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 |