Intelligent Recommender Systems
| Course Code | 321-6050 |
|---|---|
| Semester | 8 |
| ECTS | 5.00 |
| Hours (Theory) | 3 |
| Hours (Lab) | |
| Instructor | Symeonidis Panagiotis |
Course Content
Cooperative Filtering
Content and Semantics-based Recommendation Systems
Recommendation Systems based on Graph Data
Deep Neural Networks
Learning Outcomes
The objectives of the course are to familiarize students with the following:
Application of knowledge and understanding:
Understanding the skills, tools and techniques required to use data science effectively.
Knowledge of techniques and methods of artificial intelligence for the implementation of intelligent systems.
Critical thinking:
Ability to independently select documentation (in the form of books, web, journals, etc.) needed to inform in a particular area.
Learning skills:
Ability to independently keep abreast of developments in the major areas of data science and AI.
Ability to deal with problems systematically and creatively and to use appropriate problem-solving techniques.
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Prerequisites
Not required.
Teaching and Learning Methods
| Activity | Semester workload |
|---|---|
| Lectures | 39 hours |
| Personal study | 83 hours |
| Final exam | 3 hours |
| Course total | 125 hours (5 ECTS) |
Assessment Methods / Grading
test in the form of a quiz, final written examination.
Teaching Language
Greek (English for Erasmus students)