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Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
---|---|---|---|---|---|---|
Pattern Recognition | FBE655 | Elective | Doctorate degree | 1 | Spring | 8 |
Associate Prof. Dr. Aysun TAŞYAPI ÇELEBİ
1) Explain the basics of supervised and unsupervised learning.
2) Describe parametric classification methods.
3) Apply parametric classification methods.
4) Interpret non-parametric classification methods.
5) Exploit non-parametric classification methods.
6) Perform classification in multivariate case.
7) Explain the necessity of dimensionality reduction.
8) Idntify the basics of clustering and the most known clustering methods.
Program Competencies | ||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
Learning Outcomes | ||||||||
1 | Low | Low | No relation | No relation | No relation | No relation | Middle | |
2 | Low | Low | No relation | No relation | No relation | No relation | Middle | |
3 | Middle | High | No relation | No relation | No relation | No relation | Middle | |
4 | Low | No relation | No relation | No relation | No relation | No relation | Middle | |
5 | Low | No relation | No relation | No relation | No relation | No relation | No relation | |
6 | No relation | Low | No relation | No relation | No relation | No relation | Middle | |
7 | Low | Low | No relation | No relation | No relation | No relation | Middle | |
8 | Low | Low | No relation | No relation | No relation | No relation | Middle |
Face to Face
None
Not Required
This course provides candidates with profound knowledge on supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, hidden Markov models.
Contribution of Midterm Examination to Course Grade |
30% |
---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Turkish
Not Required