Title Intelligent Recommender Systems
Lesson Code 321-6050
Semester 8
ECTS 5
Hours (Theory) 3
Hours (Lab) 0
Faculty Symeonidis Panagiotis

Syllabus

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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Prerequisite Courses

Not required.

Basic Textbooks

  • Panagiotis Simeonidis, (2023). Intelligent Recommendation Systems. Athens, Kalipos, Open Academic Publications. Available at: http://dx.doi.org/10.57713/kallipos-156, ISBN: 978-618-5726-37-9.
  • Aggarwal, Charu C. Recommender systems. Vol. 1. Cham: Springer International Publishing, 2016.
  • Ricci, Francesco, Lior Rokach, and Bracha Shapira. "Introduction to recommender systems handbook." Recommender systems handbook. Springer, Boston, MA, 2011. 1-35.

Teaching and Learning Methods

Activity Semester workload
Lectures 39 hours
   
Personal study 83 hours
   
Final exam 3 hours
Course total 125 hours (5 ECTS)

Student Performance Evaluation

 test in the form of a quiz, final written examination.

Language of Instruction and Examinations

Greek (English for Erasmus students)

Delivery Mode

Face-to-face