Information Package / Course Catalogue
Pattern Recognition
Course Code: EEE572
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

Learning pattern recognition techniques and application areas. To learn the basics of classification.

Course Content

Low level signal characterization of pretreatments, signal behavior and properties Simulation and attribute optimization in classifier structure under attribute distribution Defining patterns as a statistical decision problem Bayesian classifiers, artificial neural networks, fuzzy logic Size and data reduction by linear and nonlinear models Statistical learning theories Support vector machines

Name of Lecturer(s)
Learning Outcomes
1.Understanding Pattern Classification and Application Areas
2.To compare solutions developed with algorithms that can converge to human learning.
3.To be able to interpret the differences of the problems that can be provided with intuitive approaches.
4.Being able to follow the research topics developing in the field of Pattern Classification; To be able to make presentations by preparing short seminars on this subject.
5.To gain experience in reading and writing articles.
Recommended or Required Reading
1.Pattern Classification: R.O. Duda, P.E. Hart, D.G. Stork 2. Baskı, Wiley, 2000.
2.Neural networks for pattern recognition : C. M. Bishop, Oxford University Press, 1995.
3.Statistical Pattern Recognition: A. Webb, 2. Baskı, Wiley, 2002.
4.Introduction to Machine Learning: E. Alpaydın, MIT Press, 2004.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Introduction, definitions, samples, Bayesian decision theory, supervised learning
Week 2 - Theoretical & Practice
Classification
Week 3 - Theoretical & Practice
Classifiers based on Bayesian decision theory
Week 4 - Theoretical & Practice
Linear Classifiers
Week 5 - Theoretical & Practice
Non-linear Classifiers
Week 6 - Theoretical
Feature Extraction
Week 7 - Theoretical & Practice
Artificial Neural Networks, Fuzzy Logic
Week 8 - Intermediate Exam
Midterm Exam.
Week 9 - Intermediate Exam
Midterm Exam.
Week 10 - Theoretical & Practice
Decision Trees
Week 11 - Theoretical & Practice
Pattern Recognition Application
Week 12 - Theoretical & Practice
System Evaluation
Week 13 - Theoretical & Practice
Unsupervised Learning
Week 14 - Theoretical & Practice
Classification
Week 15 - Theoretical & Practice
Project presentation
Week 16 - Final Exam
Final Exam.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment3%30
Term Assignment1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory1664160
Assignment32521
Term Project1101020
TOTAL WORKLOAD (hours)201
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
OÇ-1
3
3
3
3
4
3
4
OÇ-2
4
4
4
3
5
4
3
OÇ-3
3
3
5
5
5
4
3
OÇ-4
3
3
3
3
4
3
4
OÇ-5
4
4
4
4
4
4
4
Adnan Menderes University - Information Package / Course Catalogue