SCHOOL

ENGINEERING

DEPARTMENT

ELECTRICAL AND COMPUTER ENGINEERING

LEVEL OF COURSE

UNDERGRADUATE

COURSE CODE

ECE_ΑΚ813

SEMESTER OF STUDIES

COURSE TITLE

ARTIFICIAL INTELLIGENCE IΙ

INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

TEACHING HOURS

PER WEEK

ECTS CREDITS

Lectures

3

Labs

2

Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

5

5

COURSE TYPE

general background,

special background, specialised general knowledge, skills development

Field of science

PREREQUISITE COURSES:

Artificial Intelligence I

TEACHING AND ASSESSMENT LANGUAGE:

Greek

THE COURSE IS OFFERED TO ERASMUS STUDENTS

Yes (in Greek)

COURSE WEBPAGE (URL)

http://www.ece.upatras.gr/index.php/el/undergraduate/under-courses.html

Learning outcomes

The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·    Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·    Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·    Guidelines for writing Learning Outcomes

This course builds on the material of Artificial Intelligence I, and explores cutting eage technologies like Answer Set Programming (ASP), deep learning, quantum machine learning, etc.

With the successful completion of the course students will:

·         understand the central notions and teqnichues in Knowledge Representation and Reasoning (for example, non-monotonic reasoning. belief change, reasoning about action, etc).

·         be able to represent and solve problems with ASP

·         use machine learning based on convolutional neural networks

·         understand the basic principles of the operation of AI in quantum computers.

General Abilities

Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

Others

·         Search for, analysis and synthesis of data and information, with the use of the necessary technology.

·         Adapting to new situations

·         Decision-making

·         Knowledge Representation and Reasoning - Foundations

o    Non-monotonic reasoning

o    Reasoning about Action

o    Belief Change

o    Planning

o    Semantic Web Technologies

·         Neural Networks

o    Perceptron, Multi-layer Perceptron

o    Error Correction Training

o    Introduction to Deep Neural Networks

o    Convolutional Neural Networks

o    Modern Architectures for Deep Learning Neural Networks

o    Applications of Deep Learning

·         Quantum Artificial Intelligence

o    Principles of Quantum Computers

o    Problem Solving with Quantum Computers

o    Quantum Machine Learning

TEACHING METHOD
Face-to-face, Distance learning, etc.

Face to face and/or distance learning

USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES
Use of ICT in teaching, laboratory education, communication with students

The learning process is supported by an electronic learning space at eclass.upatras.gr, and the use of zoom and kahoot.

TEACHING ORGANIZATION

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

STUDENT ASSESSEMNT

Description of the evaluation procedure

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

1.       Each laboratory exercise is marked with either a pass or a fail. Students need a pass for least 50% of their lab exercise to be eligible to participate in the final exam.

2.       Tutorial exercises are multiple-choice questions submitted to eclass. They do not receive a mark (they are only for practice).

3.       A final exam that determines the overall grade for the course.

·         S. Russell, P. Norvig, "Τεχνητή Nοημοσύνη - Μια σύγχρονη προσέγγιση", 4η Αμερικανική Έκδοση, Κλειδάριθμος, 2021.

·         Ι. Βλαχάβας, κ.α., "Τεχνητή Nοημοσύνη", 4η Έκδοση, Πανεπιστήμιο Μακεδονίας, 2020.

·         M. Negnevitsky, "Τεχνητή Nοημοσύνη", 3η Έκδοση, Τζιόλα, 2017.

·         V. Lifschitz, "Answer Set Programming", Springer, 2019.

·         P. Wittek, "Quantum Machine Learning", Academic Press, 2014.