Courses

Intelligent Systems

General

Course Contents

• Introduction to Intelligent systems
• Fuzzy Logic – Fuzzy Sets
• Participation Functions, Mathematical Representation
• Transactions between Fuzzy Sets (application of operators)
• Relationships between Fuzzy Sets, Fuzzy Inference
• Export rules (clustering, k-means algorithm)
• Fuzzy Conclusion (modus ponens, Synthetic Rule of Conclusion)
• Artificial Neural Networks
• Perceptron, Convergence Theorem
• Linear Neural Networks
• Feedforward networks
• Backpropagation learning algorithm
• Deep learning
• Matlab Software / Matlab Toolbox

Educational Goals

The aim of the course is to teach students both the necessary theoretical knowledge of intelligent systems as well as allow them to get familiar with practical laboratory tools.
Upon successful completion of the course students will:
– have knowledge of the basic concepts in the field of intelligent systems
– be able to apply knowledge in practice, search, analyze and synthesize data and information using the necessary technologies
– define, analyze and describe the development of an intelligent system in one or more applications that have been taught
-distinguish the characteristics of a problem which will lead them to its successful modelling
– produce solutions based on techniques of fuzzy systems and neural networks
– be able to follow the basic principles of systems development with the technologies that have been taught to compose and propose appropriate applications.

General Skills

Research, analysis and synthesis of data and information
Using corresponding technologies
Setting objectives
Project design
Setting priorities
Decision making
Monitoring results
Autonomous work
Developing new research ideas
Adherence to good practice guidelines

Teaching Methods

Lectures, Exercises, Laboratory, Project assignments, Online guidance, Projected presentations, E-mail communication, Online synchronous and asynchronous teaching platform (moodle), Interactive teaching

Students Evaluation

Assessment Language: English / Greek
The final grade of the course is formed by 70% by the grade of the theoretical part and by 30% by the grade of the laboratory part.
1. The grade of the theoretical part is formed by a written final examination and project.
The written final examination of the theoretical part may include:
Solving problems of applying the acquired knowledge, Short answer questions, multiple choice questions.
2. The examination of the Laboratory Exercises is carried out with laboratory progress in the middle of the semester and laboratory examinations at the end of the semester.

Recommended Bibliography

1. P. Tzionas. Intelligent Control, Tools and Applications. (in Greek)
2. I. Vlachavas, P. Kefalas, N. Vassiliadis, F. Kokkoras, I. Sakellariou. Artificial Intelligence – Third Edition, University of Macedonia Publications, ISBN: 978-960-8396-64-7, 2006/2011. (in Greek)
3. Diamantaras, K. (2007). Artificial Neural Networks. Athens, Greece