Laurent Vanbever: Katalogdaten im Frühjahrssemester 2020

Name	Herr Prof. Dr. Laurent Vanbever
Lehrgebiet	Vernetzte Systeme
Adresse	Inst. f. Techn. Informatik u. K. ETH Zürich, ETZ G 90 Gloriastrasse 35 8092 Zürich SWITZERLAND
Telefon	+41 44 632 70 04
E-Mail	lvanbever@ethz.ch
URL	https://nsg.ee.ethz.ch
Departement	Informationstechnologie und Elektrotechnik
Beziehung	Ausserordentlicher Professor

Nummer	Titel	ECTS	Umfang	Dozierende
227-0120-00L	Communication Networks	6 KP	4G	L. Vanbever
Kurzbeschreibung	At the end of this course, you will understand the fundamental concepts behind communication networks and the Internet. Specifically, you will be able to: - understand how the Internet works; - build and operate Internet-like infrastructures; - identify the right set of metrics to evaluate the performance of a network and propose ways to improve it.
Lernziel	At the end of the course, the students will understand the fundamental concepts of communication networks and Internet-based communications. Specifically, students will be able to: - understand how the Internet works; - build and operate Internet-like network infrastructures; - identify the right set of metrics to evaluate the performance or the adequacy of a network and propose ways to improve it (if any). The course will introduce the relevant mechanisms used in today's networks both from an abstract perspective but also from a practical one by presenting many real-world examples and through multiple hands-on projects. For more information about the lecture, please visit: https://comm-net.ethz.ch
Skript	Lecture notes and material for the course will be available before each course on: https://comm-net.ethz.ch
Literatur	Most of course follows the textbook "Computer Networking: A Top-Down Approach (6th Edition)" by Kurose and Ross.
Voraussetzungen / Besonderes	No prior networking background is needed. The course will include some programming assignments (in Python) for which the material covered in Technische Informatik 1 (227-0013-00L) and Technische Informatik 2 (227-0014-00L) will be useful.
227-0559-10L	Seminar in Communication Networks: Learning, Reasoning and Control Findet dieses Semester nicht statt. Number of participants limited to 24.	2 KP	2S	L. Vanbever, A. Singla
Kurzbeschreibung	In 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.
Lernziel	The 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.
Inhalt	The 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: https://seminar-net.ethz.ch
Skript	The slides of each presentation will be made available on the website.
Literatur	The paper selection will be made available on the course website: https://seminar-net.ethz.ch
Voraussetzungen / Besonderes	Communication 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.