COURSE UNIT TITLE

: NEURAL NETWORKS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5085 NEURAL NETWORKS 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

INSTRUCTOR ÖZLEM ÖZTÜRK

Offered to

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

Course Objective

The main objective of this course is to present various neural networks such as Multilayer Perceptrons, Radial Basis Function Networks or Self Organizing Maps and to apply them in the solution of engineering problems.

Learning Outcomes of the Course Unit

1   Describe the relation between real brains and simple artificial neural network models
2   Have detailed knowledge about various artificial neural networks
3   Analyze the performance of artificial neural networks
4   Understand the differences among various network types
5   Apply artificial neural networks in practical problems

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Neural Networks and their History. Biological Neurons and Neural Networks. Artificial Neurons
2 Networks of Artificial Neurons. Single Layer Perceptrons. Learning and Generalization in Single Layer Perceptrons
3 Hebbian Learning. Gradient Descent Learning
4 The Generalized Delta Rule. Practical Considerations
5 Learning in Multi-Layer Perceptrons. Back-Propagation
6 Learning with Momentum. Conjugate Gradient Learning
7 Bias and Variance. Under-Fitting and Over-Fitting, MIDTERM
8 Improving Generalization
9 Applications of Multi-Layer Perceptrons
10 Radial Basis Function Networks: Introduction
11 Radial Basis Function Networks: Algorithms, Applications Comittee MAchines
12 Self Organizing Maps: Fundamentals, Algorithms and Applications
13 Learning Vector Quantization (LVQ)
14 Student Presentations

Recomended or Required Reading

Textbook(s): Haykin S, (1999) Neural Networks: A Comprehensive Foundation, Prentice Hall
Supplementary Book(s): Gurney K, (1997) An Introduction to Neural Networks, Routledge.
Bishop C. M., (1995) Neural Networks for Pattern Recognition, Oxford University Press.

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, 4, 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

Tuesday 13.00 - 15:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation for final exam 1 8 8
Reading 4 15 60
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 8 8
Preparing presentations 2 6 12
Design Project 1 30 30
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 192

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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