Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
EEE 5073	STATISTICAL PATTERN RECOGNITION	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR METEHAN MAKINACI

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
Biomedical Tehnologies (English)
ELECTRICAL AND ELECTRONICS ENGINEERING
ELECTRICAL AND ELECTRONICS ENGINEERING
ELECTRICAL AND ELECTRONICS ENGINEERING

Course Objective

This course aims to introduce the basic theories, algorithms and practical applications of statistical pattern recognition.

Learning Outcomes of the Course Unit

1	Be able to understand the underlying principles and concepts of statistical pattern recognition systems.
2	Be able to extract discriminatory features from data.
3	Be able to select most appropriate classifier for a given classification problem.
4	Be able to explain the principles of supervised and unsupervised learning, and generalization ability.
5	Be able to design and evaluate the performance a pattern recognition system for a given task.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to Pattern Recognition
2	Statistical Decision Theory-I
3	Statistical Decision Theory-II (Assignment)
4	Parameter Estimation
5	Problems of Dimensionality (Assignment)
6	Component analysis and Discriminants
7	Nonparametric Techniques (Assignment)
8	Linear Discriminant Functions
9	Mid-term Examination
10	Support Vector Machines (Assignment)
11	Neural Networks
12	Decision Trees (Assignment)
13	Algorithm Independent Machine Learning
14	Unsupervised Learning and Clustering (Assignment)

Recomended or Required Reading

Textbook:
Pattern Classification: R.O. Duda, P.E. Hart, D.G. Stork 2. Baskı, Wiley, 2000

Supplementary Book(s):
Neural Networks for Pattern Recognition : C. M. Bishop, Oxford University Press, 1995
Statistical Pattern Recognition: A. Webb, Wiley, 2002
Introduction to Machine Learning: E. Alpaydın, MIT Press, 2004
Introduction to Statistical Pattern Recognition, K. Fukunaga, Academic Press, 1990.
Pattern Recognition: Statistical, Structural and Neural Approaches, R. Schalkoff, John Wiley & Sons, Inc., 1992.
Algorithms for Clustering Data, A. K. Jain, R. C. Dubes, Prentice Hall, 1988.
Pattern Recognition, S. Theodoridis, K. Koutroumbas, 3rd edition, Academic Press, 2006.

Planned Learning Activities and Teaching Methods

A series of lectures on course materials will be given using PowerPoint presentations and blackboard.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MTE	MIDTERM EXAM
2	ASG	ASSIGNMENT
3	FIN	FINAL EXAM
4	FCG	FINAL COURSE GRADE	MTE * 0.25 + ASG 0.25 +FIN 0.50
5	RST	RESIT
6	FCGR	FINAL COURSE GRADE (RESIT)	MTE * 0.25 + ASG 0.25 +RST 0.50

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Learning outcomes will be evaluated by examinations and assignments.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

hatice.dogan@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	13	3	39
Preparation for midterm exam	1	15	15
Preparations before/after weekly lectures	13	5	65
Preparation for final exam	1	20	20
Preparing assignments	6	8	48
Midterm	1	3	3
Final	1	3	3
TOTAL WORKLOAD (hours)			193

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.4	PO.5	PO.11
LO.1	4
LO.2	3	2	3	4
LO.3	2	2	3	4
LO.4	4
LO.5	2	2	3	4

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION