19M031PA - Telecommunication Services Personalization
Course specification | ||||
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Course title | Telecommunication Services Personalization | |||
Acronym | 19M031PA | |||
Study programme | Electrical Engineering and Computing | |||
Module | Applied Mathematics, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering | |||
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. | |||
Contents | ||||
URL to the subject page | https://elearning.rcub.bg.ac.rs/moodle/course/view.php?id=1296 | |||
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. | |||
Literature | ||||
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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 |