Pattern Recognition (CMPE467) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Pattern Recognition	CMPE467	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Technical Elective Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	The objective of the course is to make student familiar with general approaches such as Bayes classification, discriminant functions, decision trees, nearest neighbor rule, neural networks for pattern recognition.
Course Learning Outcomes	The students who succeeded in this course; Describe the basic classification and clustering techniques in pattern recognition Use Bayes’ decision theory for classification Use discriminant functions for classification Use Hidden Markov Models Use neural networks Apply clustering techniques
Course Content	Bayes? decision theory, classifiers, discriminant functions and decision surfaces, estimation of parameters, hidden Markov models, nearest neighbor methods; linear discriminant functions; neural networks; decision trees; hierarchical clustering; self organizing feature maps.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Introduction	Chapter 1 (main text)
2	Bayesian Decision Theory	Chapter 2
3	Bayesian Decision Theory	Chapter 2
4	Bayesian Decision Theory	Chapter 2
5	Maximum – Likelihood and Bayesian Parameter Estimation	Chapter 3
6	Maximum – Likelihood and Bayesian Parameter Estimation	Chapter 3
7	Nonparametric Techniques	Chapter 4
8	Nonparametric Techniques	Chapter 4
9	Linear Discriminant Functions	Chapter 5
10	Linear Discriminant Functions	Chapter 5
11	Multilayer Neural Networks	Chapter 6
12	Nonmetric Methods	Chapter 8
13	Unsupervised Learning and Clustering	Chapter 10
14	Unsupervised Learning and Clustering	Chapter 10

Sources

Course Book	1. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, New York: John Wiley, 2001,
Other Sources	2. 1. R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches, Wiley, 1991.
	3. 2. S.Theodoridis, K. Koutroumbas, Pattern Recognition, Elsevier, 2003.
	4. 3. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, Wiley, 2004.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	1	5
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	3	30
Presentation	-	-
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	2	40
Final Exam/Final Jury	1	30
Toplam	7	105

Percentage of Semester Work	70
Percentage of Final Work	30
Total	100

Course Category

Core Courses
Major Area Courses
Supportive Courses	X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains adequate knowledge in mathematics, science, and subjects specific to the software engineering discipline; acquires the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.			X
2	Gains the ability to identify, define, formulate, and solve complex engineering problems; selects and applies proper analysis and modeling techniques for this purpose.				X
3	Develops the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose.					X
4	Demonstrates the ability to select, and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; uses information technologies effectively.			X
5	Develops the ability to design experiments, gather data, analyze, and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline.
6	Demonstrates the ability to work effectively both individually and in disciplinary and interdisciplinary teams in fields related to software engineering.
7	Demonstrates the ability to communicate effectively in Turkish, both orally and in writing; to write effective reports and understand written reports, to prepare design and production reports, to deliver effective presentations, and to give and receive clear and understandable instructions.
8	Gains knowledge of at least one foreign language; acquires the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions.
9	Acquires an awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself.
10	Acts in accordance with ethical principles and possesses knowledge of professional and ethical responsibilities.
11	Knows the standards used in software engineering practices.
12	Knows about business practices such as project management, risk management and change management.
13	Gains awareness about entrepreneurship and innovation.
14	Gains knowledge on sustainable development.
15	Has knowledge about the universal and societal impacts of software engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering.
16	Acquires awareness of the legal consequences of engineering solutions.
17	Applies knowledge and skills in identifying user needs, developing user-focused solutions and improving user experience.				X
18	Gains the ability to apply engineering approaches in the development of software systems by carrying out analysis, design, implementation, verification, validation, and maintenance processes.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration
Project
Report
Homework Assignments	3	4	12
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	2	10	20
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			127