19M034VIM - Artificial Intelligence in Mobile Systems

Course specification
Course title Artificial Intelligence in Mobile Systems
Acronym 19M034VIM
Study programme
Type of study
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition Radio Systems
The goal Mobile networks are part of our everyday lives, whereas the use of AI is growing rapidly. The goal of this course is to provide students with an introduction to problems and techniques of AI. In a step-by-step manner, the following AI techniques are introduced: supervised , unsupervised and reinforcement learning. It explains how these techniques can be used for future wireless networks.
The outcome Student who completes this course is expected to: * understands the basic concepts of various techniques of artificial intelligence, * understands the possibility of applying machine learning in different segments of a mobile network, * master the basic algorithms and software tools for applying the techniques of machine learning to specific problems.
Contents of lectures Overview of basic concepts of artificial intelligence and machine learning. Basic types of machine learning (unsupervised, reinforcement, supervised). Classification, regression, clustering. Validation methods. Overfitting problem. Examples of applications from the mobile systems domain.
Contents of exercises The students will have several programming projects.
  1. Haesik Kim, Artificial Intelligence for 6G, Springer International Publishing, 2022 (Original title)
  2. Yuanming Shi, Kai Yang, Zhanpeng Yang, Yong Zhou, Mobile Edge Artificial Intelligence Opportunities and Challenges, Elsevier, 2021. (Original title)
  3. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016.god (Original title)
  4. Hassoun M., Fundamentals of Artificial Neural Networks. Massachusetts MA: The MIT Press; 2003. (Original title)
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
3 1 1
Methods of teaching The students will have several programming projects.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures Test paper 30
Practical lessons Oral examination
Seminars 70