227-0559-10L  Seminar in Communication Networks: Learning, Reasoning and Control

SemesterSpring Semester 2020
LecturersL. Vanbever, A. Singla
Periodicityyearly recurring course
CourseDoes not take place this semester.
Language of instructionEnglish
CommentNumber of participants limited to 24.

AbstractIn this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. This semester the seminar will focus on topics blending networks with machine learning and control theory.
ObjectiveThe two main goals of this seminar are: 1) learning how to read and review scientific papers; and 2) learning how to present and discuss technical topics with an audience of peers.

Students are required to attend the entire seminar, choose a paper to present from a given list, prepare and give a presentation on that topic, and lead the follow-up discussion. To ensure the talks' quality, each student will be mentored by a teaching assistant. In addition to presenting one paper, every student is also required to submit one (short) review for one of the two papers presented every week in-class (12 reviews in total).

The students will be evaluated based on their submitted reviews, their presentation, their leadership in animating the discussion for their own paper, and their participation in the discussions of other papers.
ContentThe seminar will start with two introductory lectures in week 1 and week 2. Starting from week 3, participating students will start reviewing, presenting, and discussing research papers. Each week will see two presentations, for a total of 24 papers.

The course content will vary from semester to semester. This semester, the seminar will focus on topics blending networks with machine learning and control theory. For details, please see: Link
Lecture notesThe slides of each presentation will be made available on the website.
LiteratureThe paper selection will be made available on the course website: Link
Prerequisites / NoticeCommunication Networks (227-0120-00L) or equivalents. It is expected that students have prior knowledge in machine learning and control theory, for instance by having attended appropriate courses.