| Title | ΤΕΧΝΗΤΗ ΝΟΗΜΟΣΥΝΗ / ARTIFICIAL INTELLIGENCE |
| Code | NCO-04-02 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Ioannis Vlachavas |
| Common | No |
| Status | Active |
| Course ID | 40002938 |
Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)
Registered students: 269
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| GENIKĪ KATEUTHYNSĪ | Compulsory Course | 4 | 2 | 5.5 |
| Academic Year | 2021 – 2022 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Class ID | 600191454
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600191454
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=8143
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
Good level of programming, especially logic and functional programming
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, Machine Learning 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, Machine Learning, 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
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Tutorial | 13 | ✓ | |||
| Project | 50 | ✓ | |||
| Exams | 3 | ||||
| self-study | 60 | ✓ | |||
| Total | 165 |
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, Δ' Έκδοση, Εκδόσεις Πανεπιστημίου Μακεδονίας, 2020, ISBN: 978-618-5196-44-8
2. Τεχνητή Νοημοσύνη: Μια σύγχρονη προσέγγιση, S Russel, P. Norvig, ΚΛΕΙΔΑΡΙΘΜΟΣ, Δ' έκδοση,ISBN: 978-960-645-187-4
Last Update
10-09-2021