Course description

The course is designed to introduce students to theoretical concepts and practical issues associated with pattern recognition. The following topics are covered: statistical pattern recognition, liniar discriminant functions, kernel methods, support vector machine, introduction to boosting, evaluation of classifier performance. A special effort will be made to develop students' problem solving skills and engineering intuition in the subject area. Upon completion of the course, the student should be knowledgeable and competent in applying the concepts, and should be capable of reading advanced textbooks and research literature in the pattern recognition field.


Time and Location

Consult the timetable from here . top


Prof. dr. ing. Sergiu Nedevschi

Teaching Assistants




Textbook and Additional Readings

The course has a clear bibliography and required textbook. They are given at the Course Schedule section.

Occasionally, reading a research paper or other notes will be required. The papers will be available from the class web page and in paper handouts. We may also provide references and links to additional research papers as optional reading for interested students.


Lecture Attendance

You are strongly encouraged to come to the lectures. This is your opportunity to ask questions, contribute answers and insights, and affect the nature of the class. Moreover, the lecture notes and the textbook are not guaranteed to capture 100% of every topic and detail discussed during lecture.


Research Projects

Groups of students interested in research area of image processing and pattern recognition can perform a research (mini-)project.


Laboratory description

The laboratory works are compulsory.
The purpose of the laboratory works is to give the students an insight of how the knowledge provided by the lectures can be used in practical applications and to develop their skills in implementing image processing algorithms.


The lab sessions can end with homework/assignments. All assignments are due by the date indicated by the lab assistant. Solutions to the given assignments will be discussed with the laboratory assistants.


Late Assignments

All deadlines are final. No extensions, no exceptions. top


There will be one final exam (January/February 2014). Midterm exam at request. Visit the class web page for information on exact date and location. top

Grading information

  1. Written Examination (E)
  2. Lab Activity Assessment (LA)
  3. Project Assessment (PA)
Grade = 0.5 * E + 0.2 * LA + 0.3 * PA
The condition for taking the exam is: Grade>4; E>4; LA>4; PA>4;