# 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 Elective Courses Bachelor’s Degree (First Cycle) Face To Face Lecture. 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. 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 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, 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

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 30 100

### Course Category

Core Courses X

### The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Adequate knowledge in mathematics, science and computing fields; ability to apply theoretical and practical knowledge of these fields in solving engineering problems related to information systems.
2 Ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose.
3 Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4 Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in information systems engineering applications; ability to use information technologies effectively. X
5 Ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the information systems discipline. X
6 Ability to work effectively in inter/inner disciplinary teams; ability to work individually.
7 a. Effective oral and written communication skills in Turkish; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. b. Knowledge of at least one foreign language; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8 Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development.
9 a. Ability to behave according to ethical principles, awareness of professional and ethical responsibility. b. Knowledge of the standards utilized in information systems engineering applications.
10 a. Knowledge on business practices such as project management, risk management and change management. b. Awareness about entrepreneurship, and innovation. c. Knowledge on sustainable development.
11 a. Knowledge of the effects of information systems engineering applications on the universal and social dimensions of health, environment, and safety. b. Awareness of the legal consequences of engineering solutions.

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