COURSE UNIT TITLE

: FUNDAMENTALS OF NEURAL NETWORKS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EED 3107 FUNDAMENTALS OF NEURAL NETWORKS ELECTIVE 3 2 0 5

Offered By

Electrical and Electronics Engineering (English)

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR GÜLESER KALAYCI DEMIR

Offered to

Electrical and Electronics Engineering (English)

Course Objective

The aims of this course are to introduce the fundamental principles and techniques of artificial neural network systems and to Investigate the principal neural network models and applications.

Learning Outcomes of the Course Unit

1   Be able to describe basics of artificial neural networks
2   Be able to use the most common ANN models and learning algorithms for specific applications
3   Be able to identify the main implementational issues for common neural network systems
4   Be able to implement the algorithms by using a computer
5   Be able to explain the principles of supervised and unsupervised learning, and generalization ability of ANN

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

EED 2410 - SIGNALS AND SYSTEMS

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to ANN, Historical progress of ANN.
2 Fundamental Concepts and Models, General artificial Neuron Model
3 Supervised Learning, Single-Layer Discrete Valued Perceptron
4 Single layer, continuous valued perceptron. Nonlinear (sigmoidal) activation function
5 Multilayer Feedforward Networks. Backpropagation Algorithm
6 Radial Basis Function Networks
7 Deep NNs
8 Convolutional NNs
9 Convolutional NNs
10 Recurrent ANNs
11 Recurrent ANNs and Their Applications
12 Pattern recognition applications of ANNs
13 Control applications of ANNs
14 Large Language Models

Recomended or Required Reading

1) Neural Networks and Learning Machines, Simon Haykin, Prentice Hall, 2009.
2) Neural Networks and Deep Learning, Charu C. Aggarwal, Springer, 2023.

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, laboratory studies 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 * 0.25 + LAB * 0.25 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + LAB * 0.25 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Midterm exam, project, laboratory study, final exam

Language of Instruction

English

Course Policies and Rules

Policy will be announced on the course page in SAKAI system.

Contact Details for the Lecturer(s)

guleser.kalayci@deu.edu.tr
(0 232 301 7152)

Office Hours

To be announced on the couse page in SAKAI system.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Labratory 14 2 28
Preparations before/after weekly lectures 14 2 28
Preparation for final exam 1 12 12
Preparation for midterm exam 1 12 12
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 126

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.2544
LO.355
LO.4352
LO.545

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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