Advanced Pattern Recognition Tecniques and Applications
Computer Engineering (Ph.D.)
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Advanced Pattern Recognition Tecniques and Applications | BLM608 | Elective | Doctorate degree | 1 | Spring | 8 |
Name of Lecturer(s)
Prof. Dr. Yaşar BECERİKLİ
Learning Outcomes of the Course Unit
1) Propose a pattern recognition method for a specific problem
2) Analyze performance of advanced/different pattern recognition methods
3) Combine outputs of advanced/different pattern recognition methods
4) Comprehend the theoretical foundations and workings of different/advanced pattern
recognition methods
5) Modify a pattern recognition method to solve a new problem
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | High | Middle | Middle | No relation | No relation | Middle | No relation | No relation | No relation | High | No relation | No relation | |
| 2 | High | Middle | Middle | No relation | No relation | Middle | No relation | No relation | No relation | High | No relation | No relation | |
| 3 | High | Middle | Middle | No relation | No relation | Middle | No relation | No relation | No relation | High | No relation | No relation | |
| 4 | High | Middle | No relation | No relation | No relation | Middle | No relation | No relation | No relation | High | No relation | No relation | |
| 5 | High | Middle | Middle | No relation | No relation | Middle | No relation | No relation | No relation | High | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Pattern recognition Machine Learning
Course Contents
Overview of pattern recognition: Bayes decision theory/ML and Bayes parameter estimation/classification with Linear and nonlinear describtion function/Support vector machines/ Perceptron modeling, Neural networks, unsupervised learning, clustering, classification and clustering with fuzzy systems, evalotionary algoriths, applications
Weekly Schedule
1) Introduction, mathematical preliminaries;2) Pattern Recognition basics;
3) Probabilityand probability Distributions;
4) Linear Regression Models
5) Classifier based on bayes decision theory
6) Nonlinear classifiers
7) Artificial neural networks and classifiers
8) Midterm examination/Assessment
9) mid term
10) Karhunen Loeve Transformation/SVD /ICA
11) Clustering
12) Clustering based on heuristic learning
13) fuzzy logic
14) fuzzy clustering
15) applications
16) Final examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Discussion
3) Demonstration
4) Group Study
5) Self Study
6) Problem Solving
Assessment Methods and Criteria
Contribution of Semester Studies to Course Grade |
40% |
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Contribution of Final Examination to Course Grade |
60% |
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Total | 100% | |||||||||||
Language of Instruction
Turkish
Work Placement(s)
Not Required