CE 322 | Course Introduction and Application Information

Course Name
Pattern Recognition
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 322
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator
Course Lecturer(s) -
Assistant(s) -
Course Objectives The course focuses on the theory and applications of pattern recognition. The topics include an overview of the problem of pattern classification, feature extraction, object recognition, statistical decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition.
Course Description The students who succeeded in this course;
  • Design basic and advanced pattern recognition systems.
  • Explain main approaches in statistical and syntactic pattern recognition.
  • Describe main issues involved in pattern recognition system design.
  • Compare different pattern recognition techniques.
  • Apply pattern recognition techniques using computer toolboxes.
Course Content The following topics will be included: learning and adoption, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, sufficient statistics, non-parametric techniques, linear discriminants, algorithm independent machine learning, classifiers, unsupervised learning, clustering.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction to Pattern Recognition Chapter 1.Sections 1.1-1.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
2 Learning and Adoption Chapter 1.Sections 1.5,1.6. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
3 Bayesian Decision Theory Chapter 2.Sections 2.1-2.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
4 Discriminant Functions Chapter 2.Sections 2.5,2.6, 2.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
5 Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation Chapter 3.Sections 3.1-3.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
6 Sufficient Statistics Chapter 3.Sections 3.5-3.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
7 Non-Parametric Techniques Chapter 4.Sections 4.1-4.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
8 Linear Discriminant Functions Chapter 5.Sections 5.1-5.8. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
9 Midterm Exam
10 Non-MetricMethods Chapter 8.Sections 8.1-8.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
11 Algorithm-Independent Machine Learning Chapter 9.Sections 9.1-9.3. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
12 Algorithm-Independent Machine Learning – Resampling Chapter 9.Sections 9.4,9.5. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
13 Algorithm-Independent Machine Learning – Classifiers Chapter 9.Sections 9.6,9.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
14 Unsupervised Learning and Clustering Chapter 10.Sections 10.1-10.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
15 Unsupervised Learning and Clustering Chapter 10.Sections 10.5-10.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
16 Project Presentations

 

Course Notes/Textbooks

Duda, R.O.Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.

Suggested Readings/Materials

Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2007; Marsland, S. Machine Learning: An Algorithmic Perspective. CRC Press. 2009. (Also uses Python.); Theodoridis, S. and Koutroumbas, K. Pattern Recognition. Edition 4. Academic Press, 2008.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
5
10
Presentation / Jury
Project
1
20
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
7
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
Including exam week: 16 x total hours
16
3
48
Laboratory / Application Hours
Including exam week: 16 x total hours
16
Study Hours Out of Class
14
2
Field Work
Quizzes / Studio Critiques
Homework / Assignments
5
2
Presentation / Jury
Project
1
20
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
20
Final / Oral Exam
1
24
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest