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19D031PTS - Personalised Communication Services

Course specification
Course title Personalised Communication Services
Acronym 19D031PTS
Study programme Electrical Engineering and Computing
Module
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition None
      The goal Overview of the state-of-the-art and research in personalised telecommunication, esp. mobile services and applications.
      The outcome Provide students with the ability to design and analyse advanced personalised services and applications.
      Contents
      Contents of lectures Importance od personalisation. Algorithms applicable to services, applications and content personalisation. User modeling. Application of machine learning and data mining. Recommender systems. Internet search. Mobile apps. Privacy concerns. Personalisation under special and atypical usage scenarios.
      Contents of exercises Simulation and analysis of typical algorithms and protocols. Personalised application design.
      Literature
      1. Ricci, F.; Rokach, L.; Shapira, B.; Kantor, P.B. (Eds.): Recommender Systems Handbook, Springer, 2011
      2. S. Theodoridis: “Machine Learning: A Bayesian and Optimization Perspective”, Academic Press, 2015 (ISBN 9780128015223)
      3. I. Witten et al: “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2017 (ISBN 978-0128042915)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      6
      Methods of teaching Lectures and research assignments.
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons Oral examination 30
      Projects 70
      Colloquia
      Seminars