Pattern Recognition | Faculty of Technical Sciences

Subject: Pattern Recognition (17.DOM55L)

Native organizations units: Department of Fundamentals Sciences

Type of studies	Title
Doctoral Academic Studies	Mathematics in Engineering (Year: 1, Semester: Summer)
Doctoral Academic Studies	Engineering Animation (Year: 1, Semester: Summer)

General information:

Category	Scientific-professional
Scientific or art field	Applied Computer Science and Informatics Teorijska i primenjena matematika
ECTS	10

Enabling students to develop abstract thinking and acquire knowledge about pattern recognition. Other than that, the goal is to enable the student to apply theoretical foundations of the subject to problem solving in areas pertaining to a varied spectrum of scientific disciplines. The student is trained to use the corresponding functions in Matlab.

Acquired knowledge is used in professional subjects and practice. It uses translated material from recognizing forms for making mathematical models of problems from various fields of theory and application, for example in image processing.

Introduction. Resolute Functions. Pattern Recognition - Supervised Learning . Probabilistic Pattern Recognition. Syntactic Pattern Recognition. Classifying. Applications of Pattern Recognition.

Lectures. Consultations. Lectures are organized in combined form. The presentation of the theoretical part is followed by the corresponding examples which contribute to better understanding of the theoretical part. In addition to lectures there are regular consultations. The students can take partial exams during the course. A part of the course material (which represents a unit of course subject matter) is presented orally and submitted as a written seminar paper. The oral part of the final examination is eliminatory. Through research and study work the student will, on the bases of scientific journals and other relevant literature that has been studied independently, develop further understanding of the material covered in lectures. Working with the course teacher the student develops the ability to independently work on a scientific paper.

Authors	Title	Year	Publisher	Language
Bishop, C.M.	Pattern Recognition and Machine Learning	2006	Springer, New York	English
Webb, A.	Statistical Pattern Recognition	1999	Arnold, London-Sydney-Auckland	English
Duda, R. O., Hart, P. E., Stork, D. G.	Pattern Classification	2005	Willey-Interscience, New York	English

Course activity	Pre-examination	Obligations	Number of points
Lecture attendance	Yes	Yes	5.00
Theoretical part of the exam	No	Yes	40.00
Term paper	Yes	Yes	20.00
Project defence	Yes	Yes	10.00
Practical part of the exam - tasks	No	Yes	25.00