COURSE UNIT TITLE

: ARTIFICAL INTELLIGENCE AND ITS APPLICATIONS IN INDUSTRY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IND 3938 ARTIFICAL INTELLIGENCE AND ITS APPLICATIONS IN INDUSTRY ELECTIVE 3 0 0 4

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FEHMI BURÇIN ÖZSOYDAN

Offered to

Industrial Engineering

Course Objective

With this course, it is aimed to provide students of DEU Industrial Engineering Department with important artificial intelligence topics such as artificial neural networks, deep learning, machine learning, which have an extremely important place in engineering science, and their applications in production systems.

Learning Outcomes of the Course Unit

1   To understand the concept of artificial intelligence and its importance, to have information about its industrial applications
2   Being able to design, train and use Artificial Neural Networks
3   To learn and use the concept of machine learning and its fundamental algorithms
4   To learn the concept of Deep Learning and to have knowledge about fundamental deep networks

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to artificial intelligence; basic concepts
2 Classification of artificial intelligence topics, industrial applications
3 Machine learning - fundamental concepts
4 Machine learning - fundamental algorithms
5 Machine learning - fundamental algorithms
6 Machine learning applications
7 Introduction to artificial neural networks
8 Artificial neural networks; fundamental concepts
9 Single layer simple perceptrons
10 Training single layer perceptrons
11 Multilayer perceptrons
12 Gradient-based training algorithms
13 Introduction to deep learning; fundamental concepts
14 Deep learning applications

Recomended or Required Reading

Haykin, S., (2008) Neural Networks and Learning Machines, McMaster University, Hamilton, Ontario, Canada, ISBN-13: 978-0-13-147139-9, ISBN-10: 0-13-147139-2
Öztemel, E., (2016) Yapay Sinir Ağları, Papatya Yayıncılık
Çakır, F.S. (2018) Yapay Sinir Ağları, Matlab Kodları ve Matlab Toolbox Çözümleri, Nobel Akademik Yayıncılık, ISBN: 9786057928122
Rençberi Ö.F. (2018) Sınıflandırma Problemlerinde Çoklu Lojistik Regresyon, Yapay Sinir Ağ ve ANFIS Yöntemlerinin Karşılaştırılması: Insani Gelişmişlik Endeksi Üzerine Uygulama, Gazi Kitabevi ISBN: 6053446699

Planned Learning Activities and Teaching Methods

The topics covered in the course will be transferred to the students through computer-based applications, sample problem solutions and presentations on the board and students will be expected to perform these applications. The course will involve intensive coding. In addition, all the techniques described in this course will be brought together and used.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.20 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm (30%) + Project (20%) + Final Exam (50%)

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Adress: Dokuz Eylül University, Industrial Engineering Department, Tınaztepe Campus, Izmir, Türkiye
E-mail: burcin.ozsoydan@deu.edu.tr, burcin.ozsoydan@gmail.com
Tel: 0232 301 7630

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Tutorials 0 0 0
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 15 15
Preparation for final exam 1 20 20
Preparation for quiz etc. 0 0 0
Preparing presentations 1 15 15
Final 1 2 2
Midterm 1 2 2
Quiz etc. 0 0 0
TOTAL WORKLOAD (hours) 102

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.15555
LO.2555
LO.3555
LO.4555

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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