Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05051106 | Introductıon to Pattern Recognıtıon | 2025 | 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 | yok |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Neşe ÖZKAN YILMAZ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Neşe ÖZKAN YILMAZ | A BLOK-130 | [email protected] | 7812 |
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 | Knowledge of database system control and management. | P.4.11 | |
| O2 | Understanding embedded systems | P.4.16 | |
| O3 | Knowledge of operating system structures and their differences. Ability to choose the right operating system. | P.4.19 | |
| O4 | Basic Web | P.5.1 | |
| O5 | Transfer ability | P.10.6 | |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to Pattern Recognition, Learning and Adoption |
| 2 | Bayesian Decision Theory |
| 3 | Discriminant Functions |
| 4 | Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation, Adequate Statistics |
| 5 | Nonparametric Techniques |
| 6 | Linear Discriminant Functions |
| 7 | Non-Metric Methods |
| 8 | Algorithm-Independent Automatic Learning |
| 9 | Algorithm-Independent Automatic Learning – Resampling |
| 10 | Algorithm-Independent Automatic Learning – Classifiers |
| 11 | Unsupervised Learning and Grouping |
| 12 | Unsupervised Learning and Clustering |
| 13 | Unsupervised Learning and Clustering II |
| 14 | Final |
Textbook or Material
| Resources | Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2007; Marsland, S. Machine Learning: An Algorithmic Perspective. CRC Press |
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 | 3 | 42 |
| Midterms | 1 | 32 | 32 |
| 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 | 34 | 34 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
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 | 1 | - | - |
| O3 | Knowledge of operating system structures and their differences. Ability to choose the right operating system. | - | 2 | - |
| O4 | Basic Web | - | - | 3 |
| O5 | Transfer ability | 4 | - | - |