19M031PA - Telecommunication Services Personalization

Course specification
Course title Telecommunication Services Personalization
Acronym 19M031PA
Study programme Electrical Engineering and Computing
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition None
    The goal Introduce the students to the fundamentals and current state in personalized telecommunication services and applications. Introduce the students to the methods of content description, indexing and retrieval. Introduce them to machine learning techniques.
    The outcome The students will master the skills required to plan, draft, and design personalized applications. In particular, they will be able to produce a working prototype of such application.
    URL to the subject page
    Contents of lectures Meaning and importance of personalization. Applicable procedures. Ethical aspects and privacy protection. Modeling of users and contents. Information retrieval techniques. Vector space model. Machine learning techniques. Examples of personalized applications for the web, TV, and mobile platforms - recommender systems and personal assistants.
    Contents of exercises Computer implementation and evaluation of information retrieval and machine learning algorithms. Personalized application design.
    1. M. Bjelica: "Personalized Applications: Theory and Practice", ETF, 2016.
    2. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008
    3. Ricci, F.; Rokach, L.; Shapira, B.; Kantor, P.B. (Eds.): Recommender Systems Handbook, Springer, 2011
    4. James Talbot, Justin McLean: Learning Android Application Programming: A Hands-On Guide to Building Android Applications, Addison-Wesley, 2014.
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1
    Methods of teaching Lectures, practices, tests and study research work
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 0
    Practical lessons 20 Oral examination 30
    Projects 50
    Colloquia 0
    Seminars 0