Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 3rd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | ELEKTRIK-ELEKTRONIK MüHENDISLIğI ANABILIM DALı DOKTORA (ÖNERILEN PROGRAM) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | ENGİN EYCEYURT (engineyceyurt@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | ENGİN EYCEYURT, | ||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to provide students with comprehensive knowledge and application skills about pattern recognition techniques and algorithms. The course will cover the mathematical foundations of pattern recognition problems, feature extraction, classification methods, machine learning algorithms, accuracy assessment methods and real-world applications. Students will develop their ability to identify and distinguish patterns from various types of data (images, audio, text, etc.), learn current technologies and approaches in this field and gain the ability to solve problems in various engineering and scientific fields. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Concepts related to pattern recognition will be learned. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-3 Completing and applying specific, limited or missing data with scientific methods; the ability to use information from different disciplines together. PO-6 Uses existing methods and devices in the field of Electrical and Electronics Engineering according to standards, designs experiments, conducts experiments, collects data, analyzes and interprets the results. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. PO-11 Ability to communicate effectively in Turkish, both verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and have the ability to give and receive clear and understandable instructions. |
Examination |
| LO-2 | Be able to define feature selection methods. |
PO-1 Conducting scientific research on subjects specific to the Electrical and Electronics Engineering discipline, interpreting this information and gaining application skills. PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. PO-9 Has knowledge about the universal and societal effects of engineering practices on health, environment and safety and the contemporary problems reflected in the field of engineering. PO-11 Ability to communicate effectively in Turkish, both verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and have the ability to give and receive clear and understandable instructions. |
Examination Term Paper |
| LO-3 | There will be an opportunity to work together with other branches. |
PO-2 Ability to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in engineering applications; has the ability to use information technologies effectively. PO-3 Completing and applying specific, limited or missing data with scientific methods; the ability to use information from different disciplines together. PO-4 Demonstrates effective skills, self-confidence in taking responsibility and teamwork, both individually and in multi-disciplinary teams, both nationally and internationally. PO-7 Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology and constantly renew oneself. PO-9 Has knowledge about the universal and societal effects of engineering practices on health, environment and safety and the contemporary problems reflected in the field of engineering. |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| This course is built on fundamental concepts such as feature extraction, dimensionality reduction, and decision functions. In addition to mathematical background, determination of optimum decision criteria and implementation of training algorithms (forward and feedback learning) will also be covered. Pattern recognition processes using supervised and unsupervised learning methods and artificial neural networks will be examined, and comparisons will be made with statistical pattern recognition. In addition, the logic and application areas of fuzzy classifiers will be discussed, providing a comprehensive understanding of current techniques in these areas. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Basic concepts and mathematical background | Lecture, question and answer, discussion |
| 2 | Feature extraction and feature selection | Lecture, question and answer, discussion |
| 3 | Feature selection | Lecture, question and answer, discussion |
| 4 | Data transformation | Lecture, question and answer, discussion |
| 5 | Data size reduction | Lecture, question and answer, discussion |
| 6 | Decision functions | Lecture, question and answer, discussion |
| 7 | mid-term exam | |
| 8 | Supervised and unsupervised learning | Lecture, question and answer, discussion |
| 9 | Feedforward and feedback learning | Lecture, question and answer, discussion |
| 10 | Pattern recognition with artificial neural networks | Lecture, question and answer, discussion |
| 11 | Statistical comparison of pattern recognition | Lecture, question and answer, discussion |
| 12 | Fuzzy classifiers | Lecture, question and answer, discussion |
| 13 | Applications of fuzzy classifiers | Lecture, question and answer, discussion |
| 14 | General review and final exam preparation | Lecture, question and answer, discussion |
| 15 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Introduction to Pattern Recognition: A MATLAB Approach, S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Academic Press, 2010. | |
| 2 | Pattern Classification: Neuro-Fuzzy Methods and Their Comparision, Shigeo Abe, Springer Verlag, 2001. | |
| Required Course instruments and materials | ||
| Textbook, projector, personal computer and python software | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | |||
| 9.Project | |||
| final exam | 16 | 1 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 3 | 14 | 42 |
| b) Search in internet/Library | 3 | 10 | 30 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 4 | 5 | 20 |
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 2 | 7 | 14 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 4 | 7 | 28 |
| final exam | 2 | 1 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||