| TYPE OF COURSE UNIT | Elective Course |
| LEVEL OF COURSE UNIT | Master's Degree With Thesis |
| YEAR OF STUDY | - |
| SEMESTER | - |
| NUMBER OF ECTS CREDITS ALLOCATED | 10 |
| NAME OF LECTURER(S) | Associate Professor Mustafa Sert
|
| LEARNING OUTCOMES OF THE COURSE UNIT |
At the end of this course, the students;
1) Know hypotheses and version spaces.
2) Know how to design a learning system.
3) Know how to apply machine learning techniques to specific problems.
|
| MODE OF DELIVERY | Face to face |
| PRE-REQUISITES OF THE COURSE | No |
| RECOMMENDED OPTIONAL PROGRAMME COMPONENT | None |
| COURSE DEFINITION | Machine learning paradigms. Concept learning, general-to-specific ordering and version spaces. Decision tree learning. Artificial neural networks, perceptrons, and multilayer networks. Bayesian learning. Instance-based learning. Support vector machine. Genetic algorithms. Applications to selected problems. |
| COURSE CONTENTS | | WEEK | TOPICS |
|---|
| 1st Week | Machine learning paradigms. | | 2nd Week | Concept learning, general-to-specific ordering and version spaces. | | 3rd Week | Concept learning, general-to-specific ordering and version spaces. | | 4th Week | Decision tree learning. | | 5th Week | Decision tree learning. | | 6th Week | Artificial neural networks, perceptrons, and multilayer networks. | | 7th Week | Artificial neural networks, perceptrons, and multilayer networks. | | 8th Week | Mid-term | | 9th Week | Bayesian learning. | | 10th Week | Instance-based learning. | | 11th Week | Support vector machine. | | 12th Week | Genetic algorithms. | | 13th Week | Genetic algorithms. | | 14th Week | Applications to selected problems. |
|
| RECOMENDED OR REQUIRED READING | 1. Machine Learning, Tom Mitchell, McGraw-Hill.
2. Pattern Recognition and Machine Learning, Christopher Bishop, Springer.
3. Pattern Classification, Richard O. Duda, Wiley.
|
| PLANNED LEARNING ACTIVITIES AND TEACHING METHODS | Lecture,Questions/Answers,Practice,Problem Solving,Project,Report Preparation,Presentation |
| ASSESSMENT METHODS AND CRITERIA | | | Quantity | Percentage(%) |
|---|
| Mid-term | 1 | 30 | | Quiz | 1 | 15 | | Project | 1 | 15 | | Total(%) | | 60 | | Contribution of In-term Studies to Overall Grade(%) | | 60 | | Contribution of Final Examination to Overall Grade(%) | | 40 | | Total(%) | | 100 |
|
| ECTS WORKLOAD |
| Activities |
Number |
Hours |
Workload |
| Midterm exam | 1 | 1 | 1 | | Preparation for Quiz | | | | | Individual or group work | | | | | Preparation for Final exam | 1 | 30 | 30 | | Course hours | 14 | 3 | 42 | | Preparation for Midterm exam | 1 | 20 | 20 | | Laboratory (including preparation) | | | | | Final exam | 1 | 2 | 2 | | Homework | 2 | 30 | 60 | | Project | 2 | 70 | 140 | | Total Workload | | | 295 |
|---|
| Total Workload / 30 | | | 9,83 |
|---|
| ECTS Credits of the Course | | | 10 |
|---|
|
| LANGUAGE OF INSTRUCTION | Turkish |
| WORK PLACEMENT(S) | No |
| | |
