BİL443

School of Engineering \ Computer Engineering

3

6

Elective

Turkish

BİL470 or YAP470

Computer Engineering

Machine learning, parameter estimation, llinear classification, clustering, Bayes decision theory, nonparametric techniques, artificial neural networks, support vector machines, pattern recognition applications

Pattern Recognition: S. Theodoridis, K. Koutroumbas 4. Edition Academic Press, 2009, ISBN:978-1-59749-272-0

The aim of this course is to learn about pattern recognition techniques, classification basics and application areas.

1. Understand pattern recognition systems and their features.
2. Learn different classifiers and understand what types of problems they can be used in
3. Obtain basic information about Artificial Neural Networks and Deep Learning Models
4. Create a project that includes all stages to implement a pattern recognition application
5. Prepare a written report for the project
6. Make an oral presentation about the project

Week 1: Introduction, definitions, examples, Bayesian decision theory, supervised learning
Week 2: Classifiers based on Bayesian decision theory
Week 3: Linear Classifiers
Week 4: Nonlinear Classifiers
Week 5: Feature extraction and linear transformations
Week 6: Artificial Neural Networks
Week 7: Deep Learning Models
Week 8: Decision trees
Week 9: Support vector machines
Week 10: System evaluation
Week 11: Applications of pattern recognition
Week 12: Unsupervised learning, clustering