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 5016 PATTERN RECOGNITION ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR DERYA BIRANT

Offered to

Computer Engineering (Non-Thesis-Evening) (English)
Computer Engineering Non-Thesis (English)
Computer Engineering (English)
Computer Engineering (English)
COMPUTER ENGINEERING (ENGLISH)

Course Objective

This course introduces 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 and dimensionality reduction 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
2 Classification
3 Feature Selection, Data Transformation and Dimensionality Reduction
4 Neural Networks
5 Deep Learning
6 Support Vector Machines
7 Bayes Decision Theory, K-Nearest Neighbor
8 Applications of Pattern Recognition
9 Image Recognition (Image Processing, Character Recognition, Face Recognition, Object Detection)
10 Speech Recognition (Signal Processing, Text to Speech, Speech to Text)
11 Video-Based Pattern Recognition
12 Computer Vision
13 Unsupervised Learning
14 Project Presentations

Recomended or Required Reading

Textbook: S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press, 2008, ISBN: 978-1597492720

Supplementary Book(s): C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, 2011, ISBN: 978-0387310732
G. Dougherty, Pattern Recognition and Classification: An Introduction, Springer, 2013, ISBN: 978-1461453222

Planned Learning Activities and Teaching Methods

Lectures
Research / Literature Review
Application Development
Homeworks
Presentation
Term Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 PRS PRESENTATION
3 FCG FINAL COURSE GRADE ASG * 0.50 + PRS * 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

Course outcomes will be evaluated with the presentation of the student about a topic, literature review, and project / report prepared by the student in addition to the assigned homeworks throughout the semester

Language of Instruction

English

Course Policies and Rules

Code writing knowledge and skills are required.
Participation is mandatory.

Contact Details for the Lecturer(s)

Prof.Dr. Derya BIRANT
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus, 35390 Izmir, Türkiye
Tel: 232-3017401
E-mail: derya@cs.deu.edu.tr

Office Hours

Tuesday 13:30 - 14:30

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Web Search and Library Research 1 5 5
Reading 10 3 30
Project Preparation 1 65 65
Preparing presentations 1 30 30
Preparing report 1 25 25
TOTAL WORKLOAD (hours) 225

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.145
LO.243
LO.355243
LO.441
LO.542114135

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

Copyright 2015 DEÜ. BİD

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