CENG562 MACHINE LEARNING
| Course Code: | 5710562 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Computer Engineering |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Assoc.Prof.Dr. ŞEYDA ERTEKİN BOLELLİ |
| Offered Semester: | Fall or Spring Semesters. |
Course Objectives
At the end of this course, students will be able to:
- Use major machine learning techniques and tools that are covered in the course.
- Apply major techniques confidently based on the type of available data.
- Point out the advantages and disadvantages of various solutions.
- Evaluate and compare existing solutions in terms of efficiency and adequacy.
- Design and implement machine learning solutions to realistic problems.
Course Content
Paradigms of machine learning. Inductive, deductive, abductive forms of learning. Cognitive aspects of learning. Connectionist models of learning. Programming environments for learning programs.
Course Learning Outcomes
Student that pass the course satisfactorily will be able to:
- Distinguish between different categories of machine learning algorithms.
- Identify a suitable machine learning algorithm for a given application or task.
- Design and build practical solutions to problems with realistic requirements.
- Analyze, compare and differentiate between various models by identifying, assessing and reasoning about their advantages and disadvantages.
- Run and evaluate the performance of a range of algorithms on real datasets.