EE 525

Course ID:

Course Code & Number

EE 525

Course Title

Pattern Recognition

Level

MS

Credit Hours/ ECTS Credits

(3+0+0) 3 TEDU Credits, 7.5 ECTS Credits

Year of Study:

Master

Semester:

Fall

Type of Course:

Elective

Mode of Delivery:

Face-to-face

Language of Instruction:

English

Pre-requisite / Co-requisite::

Catalog Description

Introduction to machine perception. Parameter estimation and supervised learning. Bayesian decision theory. Non-parametric approaches; Parzen windows and nearest neighbor. Linear discriminant functions. Feature extraction/selection. Pattern recognition via neural networks. Unsupervised learning and clustering.

Course Objectives

This course aims to equip students with an understanding of pattern recognition, machine learning, and their applications. The course enables automated pattern identification, prediction, and decision-making through practical techniques and algorithms.

Software Usage

MATLAB

Course Learning Outcomes

Upon successful completion of this course, students will be able to:
(1) Recall the key terms, concepts, and algorithms used in pattern recognition, including basic recognition techniques,
(2) Explain the basic principles, process steps, methods, algorithms, and real-world applications of pattern recognition,
(3) Apply pattern recognition algorithms, feature extraction, and data analysis to classify patterns and solve practical problems,
(4) Evaluate parameter effects on pattern recognition, distinguish error types, and understand their implications,
(5) Assess algorithm performance and strengths/limitations using metrics for judging suitable methods for diverse data, 
(6) Design new applications that leverage pattern recognition concepts for a complex real-world problem.

Learning Activities and Teaching Methods:

Telling/Explaining Discussion/Debate Questioning Reading Demonstrating Problem Solving Inquiry Collaborating Case Study/Scenarion Analysis Brainstorming Web Searching

Assessment Methods and Criteria:

Test / Exam Case Studies / Homework

Assessment Methods and Criteria Others:

Design Content

Recommended Reading

Required Reading

(1) Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
(2) Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification (2nd ed.). Wiley Inc.

Grading

Test/Exam (60%), Case Studies / Homework (40%)

Learning Activities and Teaching Methods Others:

Course Coordinator:

Kurtuluş Erinç Akdoğan

Student Workload:

Workload	Hrs
Lectures	42
Course Readings	70
Exams/Quizzes	70
Case Study Analysis	43

Course & Program Learning Outcome Matching: