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Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
---|---|---|---|---|---|---|
Machine Learning Fundamentals | YZM324 | Elective | Bachelor's degree | 3 | Spring | 5 |
Assistant Prof. Dr. Kaplan KAPLAN
Assistant Prof. Dr. İrfan KÖSESOY
1) Knows the fundamentals of machine learning algorithms and comprehends the computational logic.
2) Knows the terms used to measure learning performance.
3) Understand the classification, clustering and association analysis.
4) Understands the types of learning.
5) Knows the concept of machine learning.
6) Explain the concepts of data and information and know their differences.
Program Competencies | |||||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
Learning Outcomes | |||||||||||||
1 | High | High | Low | Middle | Low | Low | Low | Low | Low | Low | Low | Low | |
2 | High | Middle | Low | High | Middle | Low | Middle | High | Middle | Low | Middle | Low | |
3 | High | High | Low | Middle | Low | Low | Low | Low | Middle | Low | Middle | Low | |
4 | High | High | Middle | High | Middle | Low | Middle | High | Middle | Low | Middle | Low | |
5 | Middle | Middle | Low | Middle | Low | Low | Middle | High | Middle | Low | Middle | Low | |
6 | Middle | Middle | Low | Middle | Low | Low | Middle | High | Middle | Low | Middle | Low |
Face to Face
None
Not Required
Introduction to machine learning, Supervised (supervised, teacher) learning, classification algorithms, Support vector machines, decision trees, Layout and model selection, Online learning and perceptron algorithm, Unsupervised learning, k-mean clustering, Gaussian blend model, Maximum expectation algorithm, Principal component analysis, Independent component analysis, Reinforcement learning.
1) Lecture
2) Lecture
3) Lecture
4) Question-Answer
5) Question-Answer
6) Question-Answer
Contribution of Project to Course Grade |
30% |
---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Turkish
Not Required