Course Name Code Semester T+U Hours Credit ECTS
Pattern Recognition BSM 456 8 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi SERAP ÇAKAR
Course Lecturers
Course Assistants
Course Category
Course Objective Recently, many applications based on manufacturing includes pattern recognition techniques. Industrial part, fingerprint, signature, face, iris and retina recognition are some of them.

At the end of this course, it is aimed that the students should understand pattern recognition concepts and to design a pattern recognition system for any kind of application. They also should know the problem solution steps of a pattern recognition system.
Course Content The definition of the patterns and basic concepts. Pattern classes. Feature extraction. Pattern classification techniques. Statistical pattern classification. Introduction to machine learning. Pattern classification using machine learning. Learning types in machine learning. Performance analysis in pattern recognition. Sample applications (Fingerprint, industrial part recognition, signature and character recognition)
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Understand the fundamentals of pattern recognition Lecture, Drilland Practice, Project Based Learning, Testing, Homework, Project / Design,
2 Comprehend actual pattern recognition applications Lecture, Self Study, Testing, Homework,
3 Comprehend pattern classification methods Lecture, Drilland Practice, Self Study, Project Based Learning, Testing, Homework, Project / Design,
4 Design a pattern recognition system Lecture, Self Study, Problem Solving, Project Based Learning, Oral Exam, Homework, Project / Design,
Week Course Topics Preliminary Preparation
1 The definition of the pattern, basic concepts, pattern classes
2 Feature vectors.
3 Pattern classification techniques, statistical pattern classification.
4 Statistical pattern classification
5 The prediction of the probabilistic density functions
6 Bayesian decision theory, maximum likelihood theory
7 Introduction to machine learning. Supervised, unsupervised and reinforced learning
8 Pattern recognition based on machine learning.
9 Error analysis on the classification
10 Reliability on the classification
11 Design of a sample pattern recognition system
12 The software and hardware architecture of a pattern recognition system, sensors
13 Sample applications and presentations by students.
14 Sample applications and presentations by students.
Course Notes Lecture Notes
Course Resources 1-AWCOCK G.J. and THOMAS R., Applied Image Processing, McGraw-Hill, Inc., 1996.
2-TYETER D.R. The pattern recognition basis of artifical intelligence, California: IEEE Computer Society, 1998.
3-ALTUNER M., Dönüştürüceler, Erciyes Üniversitesi Mühendislik Fakültesi, 1996.
4-DEVROYE L. GYORFI L., LUGOSI G., "A Probabilistic Theory of Pattern Recognition", Springer-Verlag New York, 1996.
5-AKDENİZ F., Olasılık ve istatistik, Baki kitabevi, Adana, 1998.
6-JAHNE B., Digital Image Processing, Springer Verlag, Berlin, 2005.
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions, X
2 An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations X
3 An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently, X
4 An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods, X
5 An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research, X
6 Understanding, awareness of administration, control, development and security/reliability issues about information technologies, X
7 An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility, X
8 An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language, X
9 An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology, X
10 Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society,
11 An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications, X
12 An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 65
1. Kısa Sınav 5
2. Kısa Sınav 5
1. Ödev 25
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Quiz 2 4 8
Assignment 1 8 8
Final examination 1 15 15
Total Workload 137
Total Workload / 25 (Hours) 5.48
dersAKTSKredisi 5