CE 322 | Course Introduction and Application Information - Department of Software Engineering

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
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Problem Solving Application: Experiment / Laboratory / Workshop Lecture / Presentation
National Occupation Classification	-
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.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	will be able to design basic and advanced pattern recognition systems.
2	will be able to explain main approaches in statistical and syntactic pattern recognition.
3	will be able to describe main issues involved in pattern recognition system design.
4	will be able to compare different pattern recognition techniques.
5	will be able to apply pattern recognition techniques using computer toolboxes.

Course Description

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

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction to Pattern Recognition, Learning and Adoption	Chapter 1.Sections 1.1-1.6. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
2	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.
3	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.
4	Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation, Sufficient Statistics	Chapter 3.Sections 3.1-3.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
5	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.
6	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.
7	Non-Metric Methods	Chapter 8.Sections 8.1-8.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001.
8	Midterm Exam
9	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.
10	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.
11	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.
12	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.
13	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.
14	Project Presentations
15	Semester Review
16	Final Exam

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.

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	10
Presentation / Jury
Project	1	20
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	3	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	2	28
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	1	10	10
Presentation / Jury			0
Project	1	20	20
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	24	24
		Total	150

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	-	-	X	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	-	X
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	X	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	X	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	X	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether face-to-face, remote, or hybrid).		-	-	-	-	-
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.		-	-	-	-	-
	1	Knowledge of business practices such as project management and economic feasibility analysis; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

CE 322 | Course Introduction and Application Information

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