COURSE UNIT TITLE

: INTRODUCTION TO PATTERN RECOGNITION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CME 4410 INTRODUCTION TO PATTERN RECOGNITION ELECTIVE 2 2 0 6

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

DOCTOR ÖZLEM ÖZTÜRK

Offered to

Computer Engineering

Course Objective

The aim of this course is to learn a computer (by examples) to recognize patterns in noisy data sets (e.g. input-output relations).

Learning Outcomes of the Course Unit

1   Identify where, when and how pattern recognition can be applied
2   Able to apply Bayesian Decision Theory on pattern recognition problems
3   Performs classification using Parameter Estimation
4   Performs pattern recognition using Non-parametric approaches such as Parzen Windows and K-Nearest Neighbor methods
5   Applies Linear disciminant Functions on several pattern recognition problems
6   Knows the principles of Multilayer Neural Networks and uses on various problems
7   Performs clustering using KMeans unsupervised learning method

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 Review of Probability Theorem
3 Bayes Decision Theory
4 Normal Density and Discriminant Functions
5 Maximum Likelihood and Bayesian Parameter Estimation
6 Fisher's Linear Discriminant and Expectation Maximization
7 Non-Parametric Approaches
8 Problem Solving
9 Distance Based Methods - Nearest Neighborhood Classification
10 Linear Discriminant Functions
11 Unsupervised Learning
12 Clustering
13 Student Presentations
14 Problem Solving

Recomended or Required Reading

Main Book: Duda R. O., Hart P. E., Stork D. G., (2001), Pattern Classification, John Wiley and Sons.
Supplementary Book: C. M. Bishop, (2006), Pattern Recognition and Machine Learning, Springer.

Planned Learning Activities and Teaching Methods

Lecturing, Problem solving, Presentation, Term project, Homework and Laboratory applications

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


Further Notes About Assessment Methods

None

Assessment Criteria

All learning outcomes will be evaluated by means of midterm and final exams.
Laboratory applications and term project will be used to evaluate the performance or learning outcomes on real problems.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Özlem Öztürk
Bilgisayar Mühendisliği Bölümü
Dokuz Eylül Üniversitesi
Tınaztepe Kampüsü,
Kaynaklar-Buca
Izmir
ozlem.ozturk@cs.deu.edu.tr
+90 232 3017417

Office Hours

Will be determined during the semester.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparation for final exam 1 6 6
Preparation for midterm exam 1 4 4
Preparations before/after weekly lectures 14 2 28
Preparation for final exam 0 6 0
Preparation for midterm exam 0 4 0
Lab Preparation 5 4 20
Preparing presentations 1 22 22
Midterm 1 1 1
Final 1 2 2
TOTAL WORKLOAD (hours) 139

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1334
LO.2443
LO.34344
LO.4434
LO.54434
LO.6443
LO.7434

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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