Computer Engineering (English)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General course introduction information

Course Code:

CENG489

Course Name:

Pattern Recognition

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	7

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. BEKİR TEVFİK AKGÜN

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:

Understanding and applying pattern recognition techniques and foundations

Course Content:

Introduction - Pattern Recognition Definitions, Data Sets, Pattern Recognition Different Paradigms, Pattern and Classes, Metric and Non-Metric Proximity Measures, Feature Extraction, Selection, Close Neighbor Classifiers and Variants, Different Approaches to the Efficient Algorithms Feature for Close Neighbors, Classification of Representations, Selection, Bayes Classifier, Decision Trees, Linear Discriminant Function, Support Vector Machines, Clustering, Clustering Big Data Sets, Combination of Classifiers, Applications Prototype Different Approaches - Document Recognition

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Will be able to design basic and advanced pattern recognition systems.
2) Will be able to explain basic statistical and syntactic pattern recognition approaches.
3) Will be able to compare different pattern recognition techniques.
2 - Skills
Cognitive - Practical
1) Will be able to describe the main issues and problems in pattern recognition system designs.
2) Will be able to apply pattern recognition techniques using computer tools.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	What is pattern recognition?	Course Notes
2)	Introduction to Probability, Bayes Rule	Course Notes
3)	Random Variables, Expected Value, Average, Variance	Course Notes
4)	Bayes Decision Rule	Course notes
5)	Naive Bayes, Bayes Networks	Course notes
6)	k-NN classifier	Course notes
7)	Regression	Course notes
8)	Midterm Exam	Course notes
9)	Clustering	Course Notes
10)	PCA	Course notes
11)	Decision Trees	Course notes
12)	Linear Classifiers	Course notes
13)	SVM	Course note
14)	Deep learning	Course notes
15)	Final Exam	Course Notes

Sources

Course Notes / Textbooks:	Theodoridis, K. Koutroumbas, Pattern Recognition, Elsevier, 4th Edition 2- R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification, 2nd edition
References:	None

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	4	3	5
Program Outcomes
1) Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Professional and ethical responsibility.
10) Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
11) Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2)	The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
6)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7)	Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge.
8)	Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9)	Professional and ethical responsibility.
10)	Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
11)	Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.

Learning Activity and Teaching Methods

Expression
Brainstorming/ Six tihnking hats
Individual study and homework
Lesson
Q&A / Discussion

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	1	% 20
Midterms	1	% 30
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	3	42
Homework Assignments	1	30	30
Midterms	1	40	40
Final	1	50	50
Total Workload			204