Computer Networks and Internet Technology (VU)
Lead by Prof. Dr. Jan Beutel.
This course teaches how the Internet, a global interconnected network of computer networks, works and how it is operated. We cover its key protocols, architecture principles, and relevant applications, along with the general principles of computer network design. Beyond the state of the art, we review specific historic design choices and assess them quantitatively, so you learn to pick the right metrics to measure performance and judge past and future designs. You also get hands-on experience with the most relevant tools of today’s Internet, so you can build and operate Internet-like infrastructure and services yourself. The lecture is accompanied by a Übung/Proseminar, where individual exercises deepen the theory and simple programming tasks add practical experience.
Details
- Winter semester
@ Tue 10:15-11:45 (VO)
@ Wed various times (UE) - Target audience: BSc CS
- ECTS: 5
- Location: Campus Technik
Low-power System Design (VU)
Lead by Prof. Dr. Jan Beutel.
Designing systems with a low energy footprint is increasingly important, driven by the rise of mobile devices and growing integration density. Applications range from miniature sensors powered by ambient energy and battery-powered smartphones to energy-efficient household appliances and datacenters. Most courses treat low power as a side topic or only from a circuit-level hardware perspective, but successful low-power design spans many disciplines: system architecture, energy sources and storage, and above all software and algorithms. This lecture treats low-power design as a first-class concern, introducing key concepts in computer architecture, wireless communication, sensing, and modeling and measurement techniques. The focus is on networked embedded systems, but the principles apply to many other classes of systems. The course is hands-on and experimentation-focused: for most of the semester, you implement a sensor networking challenge in small groups.
Details
- Winter semester
@ Wed 14:30-16:00 - Target audience: MSc CS
- ECTS: 5
- Location: Fürstenweg 176
Data-driven Analysis of Geodata (VU)
Lead by Dr. Dominik Amschwand
Machine learning (ML) has rapidly become a key tool in geoscientific research, bridging traditionally separate communities and different modelling mindsets. This VU introduces data-driven environmental modelling at the intersection of traditional ML and Bayesian statistics. After a short introduction to geoscientific practice, ML, probability theory, and statistics, the course turns to hands-on applications mostly using tabular geoscientific data: classification, clustering, feature extraction, Bayesian inference, and data fusion with Kalman filtering. Examples come from glaciers, mountain permafrost, mountain meteorology, and natural hazards. You will learn to work with quantitative geoscientific data and identify their key characteristics, to compare the modelling cultures of physically-based geosciences and data-driven ML, and to assess the strengths, limitations, and major trends of traditional ML methods in geoscience.
- Summer semester
@ Tuesday 08:30-11:00 (15 sessions) - Target audience: MSc CS and geoscience
- ECTS: 5
- Location: Fürstenweg 176