ARTIFICIAL INTELLIGENCE

Course Information
TitleΤΕΧΝΗΤΗ ΝΟΗΜΟΣΥΝΗ / ARTIFICIAL INTELLIGENCE
CodeNCO-04-02
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate, 2nd / Postgraduate
Teaching PeriodSpring
CoordinatorIoannis Vlachavas
CommonYes
StatusActive
Course ID40002938

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 238
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪCompulsory Course425.5
Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600121292
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Good level of programming, especially logic and functional
Learning Outcomes
Cognitive: Student’s training on the basic principles of Artificial Intelligence. Familiarization with various applications of Artificial Intelligence, such as Knowledge Systems, Intelligent Autonomous Systems and Multi Agent Systems. Practice on implementing and utilizing Artificial Intelligence algorithms. Skills: Acquiring the ability to solve problems using Artificial Intelligence techniques. More specifically, acquiring the ability to efficiently model real world problems and select the most appropriate methodologies and algorithms for the automatic solving of them. Familiarization with existing tools in various areas of Artificial Intelligence, such as Knowledge Systems, Planning and others
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Generate new research ideas
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Basic Principles of Artificial Intelligence, Problem Representation and Solving, Informed and Uninformed Search Algorithms. Knowledge Representation, Reasoning, System Architectures, Knowledge Systems. Automated Planning. Non–symbolic Logic (Genetic Algorithms, Neural Networks). Intelligent Agents and Distributed A.I. Systems. Machine Learning. Applications (Natural Language Processing, Computer Vision, Robotics).
Keywords
Problem Representation, Search Algorithms, Knowledge Representation, Knowledge Systems, Machine Learning
Educational Material Types
  • Notes
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Description
Slides in electronic format, software tools & videos.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39✓
Tutorial13✓
Project50✓
Exams3
self-study60✓
Total165
Student Assessment
Description
Final Exams (at the end of the semester)plus bonus grades from projects.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Exam with Extended Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Τεχνητή Νοημοσύνη, Ι. Βλαχάβας, Π. Κεφαλάς, Ν. Βασιλειάδης, Φ. Κόκκορας, Η. Σακελλαρίου, Γ' Έκδοση, Εκδόσεις Πανεπιστημίου Μακεδονίας, 2011, ISBN: 978-960-8396-64-7 2. Τεχνητή Νοημοσύνη: Μια σύγχρονη προσέγγιση, S Russel, P. Norvig, ΚΛΕΙΔΑΡΙΘΜΟΣ, ISBN: 9602098732
Last Update
11-05-2018