SCHOOL

ENGINEERING

DEPARTMENT

ELECTRICAL AND COMPUTER ENGINEERING

LEVEL OF COURSE

UNDERGRADUATE

COURSE CODE

ECE_ΑΚ705

SEMESTER OF STUDIES

COURSE TITLE

ARTIFICIAL INTELLIGENCE I

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:

Algorithms and Data Structures

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

Learning outcomes: At the end of this course, students will be able to apply AI problem-solving methods and toto use various search techniques. They will also aquired basic knowledge in the fields of proposiitonal logic, predicate logic, decision making, game theory, and machine learning.

Skills: At the lab sessions, students learn to program in Prolog and to use the machine-learning platfom Weka.

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

·         Working independently

·         Production of new research ideas

·         Introduction

o    Definition of AI

o    Brief History of AI

o    Connections with Other Disciplines

·         Search Algorithms – Constraint Satisfaction

o    State Spaces

o    Search Trees

o    Uninformed Search

o    Partially Informed Search

o    Informed Search

o    Local Search

o    Constraint propagation

o    Forward Checking

o    Arc Consistency

o    Adversarial Search

·         Game Theory

o    Games with Sequential Moves

o    Games with Simultanious Moves

o    Nash Equalibrium

o    Utility Theory and Rational Choice

·         Knowledge Representation and Reasoning - Foundations

o    Patterns of Reasoning

o    Propositional Logic

o    Resolution in Propositional Logic

o    First-Order Logic (Predicate Logic)

o    Resolution in First-Order Logic Logic

·         Machine Learning

o    Building Models

o    Decision Trees

o    Bayesian Networks

o    Probabilistic Reasoning

o    Markov Models

o    Genetic Algorithms

o    Neural Networks

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 course is supported by an electronic learning space at eclass.upatras.gr, through which students receive course material, video-recorded lectures, exercises, slides, instructions, and announcements about lectures, lab sessions, and the exams. Moreover, students are supported through 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 67% 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.