| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Machine Learning | BLM597 | Elective | Master's degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Sevinç İLHAN OMURCA
Associate Prof. Dr. Orhan AKBULUT
Learning Outcomes of the Course Unit
1) What is machine learning
2) The applications of machine learning
3) Supervised learning
4) Clustering
5) Feature selection
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | No relation | No relation | High | No relation | No relation | Middle | High | No relation | Low | Low | No relation | |
| 2 | No relation | No relation | High | No relation | No relation | Middle | High | No relation | No relation | No relation | No relation | |
| 3 | No relation | Low | High | Low | No relation | Middle | High | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | High | No relation | No relation | Middle | High | No relation | No relation | No relation | No relation | |
| 5 | No relation | No relation | High | No relation | No relation | Middle | High | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Introduction, Supervised learning,Bayesian Decision Theory, Decision Trees, Parametric methods, Multivariate methods, Dimensionality reduction, Clustering, machine learning, applications
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
70% |
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Contribution of Final Examination to Course Grade |
30% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required