INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Course Code
07.03ΔΕ
ECTS Credits
6
Semester
6th Semester
Course Category
Specialization
Business Administration
Course Description
GENERAL
TEACHING METHODS: TEACHING HOURS (WEEKLY)

Lectures

 

Tutorial

2

1
COURSE TYPE: Specialist Background (Optional)
COURSE PREREQUISITES: None
TEACHING LANGUAGE: Greek
THE COURSE IS OFFERED TO ERASMUS STUDENTS: Yes (English language - by arrangement with the teacher)
LEARNIING RESULTS
Course Description and Learning Objectives

Upon successful completion of the course, students are expected to have:

Knowledge: to understand the broader scientific field of Artificial Intelligence and the application of its methods in modern business.

Skills: to be able to adopt and apply AI methods in modern business.

Skills: to describe and apply to problems in modern business Knowledge-based Intelligent Systems, Rule-based Intelligent Systems, Fuzzy Systems, Artificial Neural Networks, Evolutionary Algorithms, Hybrid Intelligent Systems, and Knowledge Engineering techniques.
Competencies

The course aims to :

- Search, analysis and synthesis of data and information, using the necessary technologies

- Decision making

- Autonomous work

- Group work

- Promotion of free, creative and deductive thinking
CONTENT

The course is a general introduction to the scientific field of Artificial Intelligence and its application in modern business.

Course content:

Search problems

Constraint satisfaction problems

Knowledge-based intelligent systems

Experiential systems,

Fuzzy Systems

Introduction to Artificial Neural Networks

Genetic - Evolutionary Algorithms

Hybrid Intelligent Systems

Knowledge engineering

Data mining and knowledge discovery
TEACHING and LEARNING METHODS - EVALUATION
TEACHING METHOD

- Lectures in class

- Face to face - Solving tutorial exercises

Uploading material for further study and solving exercises on the e-class platform
USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES Support for the learning process through the e-class platform
METHODS OF INSTRUCTION
Method Semester workload
Lectures 26

Tutorial - Classroom exercises

Independent solution of exercises

13

39
Independent study

72

 
Total workload in hours 150
STUDENT LEARNING ASSESMENT

The evaluation will be conducted in Greek in three distinct ways:

Written final examination including:

- Three main types of test.

- Comparative evaluation of theory elements

- Problem solving
RECOMMENDED-BIBLIOGRAPHY

1. Negnevitsky Michael, Artificial Intelligence, Edition: 3rd/2017, ISBN: 978-960-418-719-5, A.TZIOLA & S.A. (Book Code in Eudoxos: 59421530)

2.VLACHAVAS I., HEAD P., ΒΑΣΙΛΕΙΑΔΗΣ N., KOKKORAS F., SAKELLARIOU I., TECHNICAL NOHOMOSYNTH - 4th EDITION, 2020, ISBN: 978-618-5196-44-8, Publisher): ASSESSMENT AND ASSET MANAGEMENT COMPANY OF THE UNIVERSITY OF MACEDONIA (Book Code in Eudoxos: 94700120)

3. W. ERTEL INTRODUCTION TO ARTIFICIAL LAW, Edition: 2/2019, ISBN: 9789603307969, Publisher: GRIGORIOS CHRYSOSTOMOU FOUNTAS. (Book Code in Eudoxos: 86053651)

4.Konstantinos Diamantaras, Dimitris Botsis, MECHANICAL LEARNING, Edition: 1/2019, ISBN: 978-960-461-995-5, KLEIDARITHMOS PUBLISHING LTD. (Book Code in Eudoxos: 86198212)

5. Haykin Simon, Neural Networks and Machine Learning, 3rd edition/2010, ISBN: 978-960-7182-64-7, Publisher: A. PAPASOTIRIOU & SIA I.K.E. (Eudox Book Code: 9743)

6.AIKATERINI GEORGULI, Artificial Intelligence, Edition: 1/2016, ISBN: 978-960-603-031-4, Publisher: Hellenic Academic Electronic Textbooks and Aids - Depository "Kallipos" Type: E-book. (Book Code in Eudoxos: 320248)