COURSE UNIT TITLE

: PATTERN RECOGNITION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EED 4010 PATTERN RECOGNITION ELECTIVE 3 2 0 6

Offered By

Electrical and Electronics Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR HATICE DOĞAN

Offered to

Electrical and Electronics Engineering

Course Objective

This course aims to introduce the fundamentals of pattern recognition techniques and application areas.

Learning Outcomes of the Course Unit

1   Be able to describe the basics of pattern recognition systems.
2   Be able to apply supervised or unsupervised learning techniques to various kinds of data and assess the outcome of the learning algorithms
3   Be able to extract features from raw data to be analysed.
4   Be able to identify the main implementational issues for pattern recognition techniques
5   Be able to use MATLAB in the design and simulation of pattern recognition systems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

EED 3005 - PROBABILITY AND RANDOM SIGNAL PRINCIPLES

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Pattern Recognition
2 Review of probability and linear algebra
3 Bayesian decision theory -I
4 Bayesian decision theory -II
5 Linear discriminants-I
6 Linear discriminants-II
7 Parametric estimation
8 Supervised learning methods
9 Unsupervised learning methods
10 Reinforcement learning methods
11 Pattern Recognition with MATLAB
12 Feature extraction for representation and classification
13 Feature selection methods
14 Evaluation of assignments.

Recomended or Required Reading

Pattern Classification 2nd ed., R.O.Duda, P.E. Hart, D.G.Stork, John Wiley& Sons, 2001
Introduction to Statistical Pattern Recognition, Keinosuke Fukunaga, Academic Press 1990.
Pattern Recognition, Sergios Theodoridis and Konstantinos Koutroumbas, Academic Press, 1998.
Pattern Recognition: Statistical, Structural and Neural Approaches by Robert J. Schalkoff, John Wiley & Sons, 1991.

Planned Learning Activities and Teaching Methods

A series of lectures on course materials will be given using PowerPoint presentations and blackboard. To support course materials, application hours will be done.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 LAB LABORATORY
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + LAB * 0.20 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + LAB * 0.20 + RST * 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

The 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)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Tutorials 14 2 28
Preparation for midterm exam 1 6 6
Preparation for final exam 1 10 10
Lab Preparation 14 2 28
Preparations before/after weekly lectures 14 3 42
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 160

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.15
LO.25422
LO.3543
LO.435
LO.54453

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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