Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Spring 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 | UĞUR SORGUCU (sorgucu@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to enable the student to learn machine learning algorithms such as Bayesian decision theory, linear discriminators, decision trees, nearest neighbour clustering, artificial neural networks, support vector machines and regression machines. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 |
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-4 Demonstrates effective skills, self-confidence in taking responsibility and teamwork, both individually and in multi-disciplinary teams, both nationally and internationally. |
Examination Performance Project |
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| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| This course covers parametric and non-parametric classification and regression tools, the theory and algorithmic applications of these algorithms. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Parametric and nonparametric machine learning methods | Lecture, question-answer, discussion ( e-learning) |
| 2 | Decision Trees | Lecture, question-answer, discussion ( e-learning) |
| 3 | Probability Models and Naive Bayes algorithm | Lecture, question-answer, discussion ( e-learning) |
| 4 | Linear Multiple Regression | Lecture, question-answer, discussion ( e-learning) |
| 5 | Linear models and perseptron algorithm. | Lecture, question-answer, discussion ( e-learning) |
| 6 | Artificial Neural Networks | Lecture, question-answer, discussion ( e-learning) |
| 7 | Artificial neural networks and back-propagation learning | Lecture, question-answer, discussion ( e-learning) |
| 8 | mid-term exam | |
| 9 | Wide margin classifiers and Lagrangian optimisation | Lecture, question-answer, discussion ( e-learning) |
| 10 | Non-linear regression methods | Lecture, question-answer, discussion ( e-learning) |
| 11 | Bayesian Networks | Lecture, question-answer, discussion ( e-learning) |
| 12 | Bayesian Networks and belief propagation algorithm | Lecture, question-answer, discussion ( e-learning) |
| 13 | Feature Selection | Lecture, question-answer, discussion ( e-learning) |
| 14 | Feature Extraction | Lecture, question-answer, discussion ( e-learning) |
| 15 | Quality measurement criteria | Lecture, question-answer, discussion ( e-learning) |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Pattern Recognition and Machine Learning, Christopher Bishop, Springer | |
| 2 | Deep Learning with Python, Francois Chollet, Manning | |
| 3 | Sinan UĞUZ, MAKİNE ÖĞRENMESİ Teorik Yönleri ve PYTHON Uygulamaları ile Bir YAPAY ZEKA Ekolü, Nobel Akademik Yayıncılık | |
| Required Course instruments and materials | ||
| Course Book Laptop Computer | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 2 | 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 | 2 | 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 | 10 | 30 |
| b) Search in internet/Library | 3 | 10 | 30 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 4 | 9 | 36 |
| mid-term exam | 3 | 1 | 3 |
| Own study for final exam | 4 | 9 | 36 |
| final exam | 3 | 1 | 3 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||