COMPUTATIONAL INTELLIGENCE
| Title | ΥΠΟΛΟΓΙΣΤΙΚΗ ΝΟΗΜΟΣΥΝΗ / COMPUTATIONAL INTELLIGENCE |
| Code | NDM-08-02 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Anastasios Tefas |
| Common | No |
| Status | Inactive |
| Course ID | 40002980 |
Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)
Registered students: 72
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| GENIKĪ KATEUTHYNSĪ | YPOCΗREŌTIKO KATA EPILOGĪ | 8 | 4 | 5 |
| Academic Year | 2020 – 2021 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600180167
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600180167
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=7944
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Prior exposition to pattern recognition, neural networks and logic can facilitate the faster comprehension of the concepts introduced.
Learning Outcomes
Cognitive: Foundation of the basic computational intelligence concepts in the areas of neural networks, statistical learning, fuzzy logic and evolutionary computation. Comprehension of the ideas behind self-organization, competitive learning, reinforcement learning. Conception of the fuzzy logic, fuzzy sets and fuzzy systems principles. Foundation of the evolutionary concepts in optimization. Exposition to the plethora of computational intelligence applications in multimedia information analysis and retrieval, intelligent control, robotics and neurofuzzy systems.
Skills: Promoting analytic and implementation skills in the taught computational intelligence fields (fuzzy theory, evolutionary computation, swarm intelligence and neural networks) and their application to real problems. Combining computational intelligence techniques for complex real problems like computer vision, multimedia information retrieval, robotic control.
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
- Work in teams
- Generate new research ideas
- Design and manage projects
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Neural Networks overview. Associative memories. Hopfiled recurrent neural networks. Self-organizing neural networks. Kohonen maps and competitive learning. Simulated annealing and Boltzmann machine. Deep Learning. Deep Convolutional Neural Networks. Deep Autoencoders. Deep Recurrent Networks. Reinforcement learning neural networks. Fuzzy logic, fuzzy learning nad fuzzy systems. Evolutionary and genetic algorithms. Swarm intelligence algorithms. Genetic programming, Socio-cognitive and mimetic algorithms. Applications in pattern recognition and robotics. Neuro-fuzzy systems. Hybrid computational intelligence systems in signal, image and video analysis.
Keywords
Computational Intelligence, Deep Learning, Fuzzy Learning, Evolutionary Algorithms
Educational Material Types
- Notes
- Slide presentations
- Multimedia
- 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
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 90 | ✓ | ✓ | ||
| Reading Assigment | 12 | ✓ | ✓ | ||
| Project | 34 | ✓ | ✓ | ✓ | |
| Written assigments | 12 | ✓ | ✓ | ✓ | |
| Exams | 2 | ✓ | ✓ | ||
| Total | 150 |
Student Assessment
Description
Project evaluation (source code, application, paper, presentation) 50%, written exams 50%. The evaluation criteria are explained in detail with the project announcement. The evaluation criteria for the exams are discussed in the last lectures of the course.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Oral Exams (Formative, Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Formative, Summative)
- Report (Summative)
Bibliography
Course Bibliography (Eudoxus)
1. ΥΠΟΛΟΓΙΣΤΙΚΗ ΝΟΗΜΟΣΥΝΗ, Σ. ΤΖΑΦΕΣΤΑΣ, Σ.ΤΖΑΦΕΣΤΑΣ, 2η Εκδοση, ΑΘΗΝΑ, 2008
2. ΝΕΥΡΩΝΙΚΑ ΔΙΚΤΥΑ ΚΑΙ ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ, 3η ΕΚΔΟΣΗ, S. Haykin, Παπασωτηρίου, 2010, Αθήνα.
3. ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ, Κ. ΔΙΑΜΑΝΤΑΡΑΣ, Δ. ΜΠΟΤΣΗΣ, ΚΛΕΙΔΑΡΙΘΜΟΣ, 2019, ΑΘΗΝΑ
Last Update
10-04-2022
