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Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Course Code Course Name Year Period Semester T+A+L Credit ECTS
05051090 Introductıon to Pattern Recognıtıon 3 Autumn 5 3+0+0 3 5
Course Type Elective
Course Cycle Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6)
Course Language Turkish
Methods and Techniques -
Mode of Delivery Face to Face
Prerequisites none
Coordinator -
Instructor(s) Assoc. Prof. Ali ÖZTÜRK
Instructor Assistant(s) -
Course Instructor(s)
Name and Surname Room E-Mail Address Internal Meeting Hours
Assoc. Prof. Ali ÖZTÜRK A-127 [email protected] 0 Thursday
14.00-15.00
Course Content
Öğrenme ve adopsiyon, Bayes karar teorisi, ayırıcı fonksiyonlar, parametrik teknikler, maksimum olabilirlik tahmini, Bayes tahmini, yeterli istatistik, parametrik olmayan teknikler, doğrusal ayırtaç fonksiyonlar, algoritma bağımsız otomatik öğrenme, sınıflandırıcılar, denetimsiz öğrenme, gruplaştırma.
Objectives of the Course
The focus of this course is on the theory and applications of pattern recognition techniques. Topics covered include machine pattern classification, feature extraction, object recognition, Bayesian decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition, and an overview of these topics is presented.
Contribution of the Course to Field Teaching
Basic Vocational Courses
Specialization / Field Courses
Support Courses X
Transferable Skills Courses
Humanities, Communication and Management Skills Courses
Relationships between Course Learning Outcomes and Program Outcomes
Relationship Levels
Lowest Low Medium High Highest
1 2 3 4 5
# Program Learning Outcomes Level
P1 Adequate knowledge in mathematics, science and related engineering discipline accumulation; theoretical and practical knowledge in these areas, complex engineering the ability to use in problems. 5
P3 Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose 4
P10 Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development 3
Course Learning Outcomes
Upon the successful completion of this course, students will be able to:
No Learning Outcomes Outcome Relationship Measurement Method **
O1 Basic Web P.5.1
O2 Knowledge of database system control and management. P.4.11
O3 Transfer ability P.10.6
O4 Understanding embedded systems P.4.16
O5 Knowledge of operating system structures and their differences. Ability to choose the right operating system. P.4.19
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7
Evaluation Method and Passing Criteria
In-Term Studies Quantity Percentage
Attendance - -
Laboratory - -
Practice - -
Course Specific Internship (If Any) - -
Homework - -
Presentation - -
Projects - -
Quiz - -
Midterms 1 40 (%)
Final Exam 1 60 (%)
Total 100 (%)
ECTS / Working Load Table
Quantity Duration Total Work Load
Course Week Number and Time 14 3 42
Out-of-Class Study Time (Pre-study, Library, Reinforcement) 14 2 28
Midterms 1 3 3
Quiz 0 0 0
Homework 0 0 0
Practice 0 0 0
Laboratory 0 0 0
Project 0 0 0
Workshop 0 0 0
Presentation/Seminar Preparation 0 0 0
Fieldwork 0 0 0
Final Exam 1 3 3
Other 0 0 0
Total Work Load: 76
Total Work Load / 30 2,53
Course ECTS Credits: 3
Course - Learning Outcomes Matrix
Relationship Levels
Lowest Low Medium High Highest
1 2 3 4 5
# Learning Outcomes P4 P5 P10
O1 Knowledge of database system control and management. 4 - -
O2 Understanding embedded systems - 5 -
O3 Knowledge of operating system structures and their differences. Ability to choose the right operating system. - - -
O4 Basic Web - - 3
O5 Transfer ability - - -