# Pattern Recognition (CMPE467) Ders Detayları

Course Name Corse Code Dönemi Lecture Hours Uygulama Saati Lab Hours Credit ECTS
Pattern Recognition CMPE467 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language İngilizce Technical Elective Courses Lisans 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 Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. X
2 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. X
3 An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.) X
4 An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively. X
5 An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. X
6 An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines. X
7 An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written, and technical drawings. X
8 An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation. X
9 Consciousness on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself. X
10 A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project. X
11 Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results of engineering solutions. X
12 Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions. X
13 Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planing, improving or changing the norms with a criticism. X

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