Electrical and Computer Engineering Graduate With Thesis
Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 80511116 | Machine Learning | 2023 | Autumn | 1 | 3+0+0 | 7,5 | 7,5 |
| Course Type | Elective |
| Course Cycle | Master's (Second Cycle) (TQF-HE: Level 7 / QF-EHEA: Level 2 / EQF-LLL: Level 7) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| 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 | - | [email protected] |
Course Content
Concept Learning, Decision tree learning, Evaluating hypothesis,Artificial neural networks, Bayesian learning, Instance-based learning, Genetic algorithms, Reinforcement learning, Evaluating and comparing the machine learning algorithms
Objectives of the Course
Teaching the machine learning concepts and algorithms, giving the ability to choose the best machine learning algorithm for a given problem, to evaluate and compare the performance of the algorithms algorithms
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Concept Learning |
| 2 | Concept Learning |
| 3 | Decision Tree Learning |
| 4 | Decision Tree Learning |
| 5 | Artificial Neural Networks |
| 6 | Artificial Neural Networks |
| 7 | Bayesian Learning |
| 8 | Bayesian Learning |
| 9 | Instance-Based Learning |
| 10 | Instance-Based Learning |
| 11 | Genetic Algorithms |
| 12 | Genetic Algorithms |
| 13 | Reinforcement Learning |
| 14 | Reinforcement Learning |
Textbook or Material
| Resources | Tom Mitchell, Machine Learning, McGraw-Hill, 1997 |
| Tom Mitchell, Machine Learning, McGraw-Hill, 1997 |
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 | 0 | 0 | 0 |
| 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 | 131 | 131 |
| Other | 1 | 10 | 10 |
| Total Work Load: | 225 | ||
| Total Work Load / 30 | 7,50 | ||
| Course ECTS Credits: | 8 | ||