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
CSE 5086 PATTERN RECOGNITION ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

INSTRUCTOR ÖZLEM ÖZTÜRK

Offered to

Computer Engineering Non-Thesis
COMPUTER ENGINEERING
Computer Engineering Non-Thesis
Computer Engineering

Course Objective

This course introduces statistical pattern recognition techniques which are useful for solving classification problems. Techniques for analyzing multidimensional data of various types and scales along with algorithms for projection, dimensionality reduction, clustering and classification of data will be explained.

Learning Outcomes of the Course Unit

1   Understand fundamental pattern recognition theories
2   Design and implement certain important pattern recognition techniques
3   Apply pattern recognition theories to applications of interest
4   Analyze classification problems probabilistically and estimate classifier performance
5   Understand and analyze methods for automatic training of classification systems

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction, Bayes Decision Theory
2 Discriminant Based Classifiers
3 Parameter Estimation
4 Maximum Likelihood Parameter Estimation
5 Expectation Maximization, A Problem of Dimensionality
6 Component Analysis and Discriminants
7 Principle Component Analysis for Face Recognition, MIDTERM
8 Nonparametric Techniques
9 K-Nearest Neighbours
10 Linear Discriminant Functions
11 Support Vector Machines
12 Neural Networks, Feature Selection
13 Unsupervised Learning
14 Student Presentations

Recomended or Required Reading

Textbook(s): Duda R O, Hart P E and Stork D G, (2001) Pattern Classification, 2nd Edition, Wiley

Planned Learning Activities and Teaching Methods

Presentations, term projects, paper research and examination

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 MTE MIDTERM EXAM
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE PRJ * 0.30 + MTE * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) PRJ * 0.30 + MTE * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Learning Outcomes (LO) 1, 2, 3, 4, and 5 will be assessed by examination. LOs 2, 3, and 5 will also be assessed by Projects.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Dr. Özlem ÖZTÜRK
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 17
e-mail: ozlem.ozturk@cs.deu.edu.tr

Office Hours

Monday 15:00 - 17:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparing presentations 2 5 10
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Design Project 1 30 30
Reading 2 10 20
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 164

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.144444444433
LO.244444444433
LO.344444444433
LO.444444444433
LO.544444444433

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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