Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Informatik Master Information
Vertiefungsfächer
Vertiefung in Distributed Systems
Wahlfächer der Vertiefung in Distributed Systems
NummerTitelTypECTSUmfangDozierende
252-0312-00LUbiquitous Computing Information W4 KP2V + 1AC. Holz, F. Mattern, S. Mayer
KurzbeschreibungUnlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses.
Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication.
LernzielUnlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses.
Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication.
SkriptCopies of slides will be made available
LiteraturWill be provided in the lecture. To put you in the mood:
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
252-0437-00LVerteilte Algorithmen Information W5 KP3V + 1AF. Mattern
KurzbeschreibungModelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate.
LernzielKennenlernen von Modellen und Algorithmen verteilter Systeme.
InhaltVerteilte Algorithmen sind Verfahren, die dadurch charakterisiert sind, dass mehrere autonome Prozesse gleichzeitig Teile eines gemeinsamen Problems in kooperativer Weise bearbeiten und der dabei erforderliche Informationsaustausch ausschliesslich über Nachrichten erfolgt. Derartige Algorithmen kommen im Rahmen verteilter Systeme zum Einsatz, bei denen kein gemeinsamer Speicher existiert und die Übertragungszeit von Nachrichten i.a. nicht vernachlässigt werden kann. Da dabei kein Prozess eine aktuelle konsistente Sicht des globalen Zustands besitzt, führt dies zu interessanten Problemen.
Im einzelnen werden u.a. folgende Themen behandelt:
Modelle verteilter Berechnungen; Raum-Zeit Diagramme; Virtuelle Zeit; Logische Uhren und Kausalität; Wellenalgorithmen; Verteilte und parallele Graphtraversierung; Berechnung konsistenter Schnappschüsse; Wechselseitiger Ausschluss; Election und Symmetriebrechung; Verteilte Terminierung; Garbage-Collection in verteilten Systemen; Beobachten verteilter Systeme; Berechnung globaler Prädikate.
Literatur- F. Mattern: Verteilte Basisalgorithmen, Springer-Verlag
- G. Tel: Topics in Distributed Algorithms, Cambridge University Press
- G. Tel: Introduction to Distributed Algorithms, Cambridge University Press, 2nd edition
- A.D. Kshemkalyani, M. Singhal: Distributed Computing, Cambridge University Press
- N. Lynch: Distributed Algorithms, Morgan Kaufmann Publ
252-0817-00LDistributed Systems Laboratory Information
Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.
W10 KP9PG. Alonso, T. Hoefler, F. Mattern, A. Singla, R. Wattenhofer, C. Zhang
KurzbeschreibungEntwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme. Das Projekt kann aus unterschiedlichen Teilbereichen (von Web-Services bis hin zu ubiquitären Systemen) stammen; typische Technologien umfassen drahtlose Ad-hoc-Netze oder Anwendungen auf Mobiltelefonen.
LernzielErwerb praktischer Kenntnisse bei Entwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme.
InhaltEntwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme. Das Projekt kann aus unterschiedlichen Teilbereichen (von Web-Services bis hin zu ubiquitären Systemen) stammen; typische Technologien umfassen drahtlose Ad-hoc-Netze oder Anwendungen auf Mobiltelefonen. Zu diesem Praktikum existiert keine Vorlesung. Bei Interesse bitte einen der beteiligten Professoren oder einen Assistenten der Forschungsgruppen kontaktieren.
263-3501-00LFuture Internet Information W6 KP1V + 1U + 3AA. Singla
KurzbeschreibungThis course will discuss recent advances in networking, with a focus on the Internet, with topics ranging from the algorithmic design of applications like video streaming to the likely near-future of satellite-based networking.
LernzielThe goals of the course are to build on basic undergraduate-level networking, and provide an understanding of the tradeoffs and existing technology in the design of large, complex networked systems, together with concrete experience of the challenges through a series of lab exercises.
InhaltThe focus of the course is on principles, architectures, protocols, and applications used in modern networked systems. Example topics include:

- How video streaming services like Netflix work, and research on improving their performance.
- How Web browsing could be made faster
- How the Internet's protocols are improving
- Exciting developments in satellite-based networking (ala SpaceX)
- The role of data centers in powering Internet services

A series of programming assignments will form a substantial part of the course grade.
SkriptLecture slides will be made available at the course Web site: https://ndal.ethz.ch/courses/fi.html
LiteraturNo textbook is required, but there will be regularly assigned readings from research literature, liked to the course Web site: https://ndal.ethz.ch/courses/fi.html.
Voraussetzungen / BesonderesAn undergraduate class covering the basics of networking, such as Internet routing and TCP. At ETH, Computer Networks (252-0064-00L) and Communication Networks (227-0120-00L) suffice. Similar courses from other universities are acceptable too.
263-3710-00LMachine Perception Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 200.
W5 KP2V + 1U + 1AO. Hilliges
KurzbeschreibungRecent developments in neural networks (aka “deep learning”) have drastically advanced the performance of machine perception systems in a variety of areas including computer vision, robotics, and intelligent UIs. This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual tasks.
LernzielStudents will learn about fundamental aspects of modern deep learning approaches for perception. Students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in learning-based computer vision, robotics and HCI. The final project assignment will involve training a complex neural network architecture and applying it on a real-world dataset of human activity.

The core competency acquired through this course is a solid foundation in deep-learning algorithms to process and interpret human input into computing systems. In particular, students should be able to develop systems that deal with the problem of recognizing people in images, detecting and describing body parts, inferring their spatial configuration, performing action/gesture recognition from still images or image sequences, also considering multi-modal data, among others.
InhaltWe will focus on teaching: how to set up the problem of machine perception, the learning algorithms, network architectures and advanced deep learning concepts in particular probabilistic deep learning models

The course covers the following main areas:
I) Foundations of deep-learning.
II) Probabilistic deep-learning for generative modelling of data (latent variable models, generative adversarial networks and auto-regressive models).
III) Deep learning in computer vision, human-computer interaction and robotics.

Specific topics include: 
I) Deep learning basics:
a) Neural Networks and training (i.e., backpropagation)
b) Feedforward Networks
c) Timeseries modelling (RNN, GRU, LSTM)
d) Convolutional Neural Networks for classification
II) Probabilistic Deep Learning:
a) Latent variable models (VAEs)
b) Generative adversarial networks (GANs)
c) Autoregressive models (PixelCNN, PixelRNN, TCNs)
III) Deep Learning techniques for machine perception:
a) Fully Convolutional architectures for dense per-pixel tasks (i.e., instance segmentation)
b) Pose estimation and other tasks involving human activity
c) Deep reinforcement learning
IV) Case studies from research in computer vision, HCI, robotics and signal processing
LiteraturDeep Learning
Book by Ian Goodfellow and Yoshua Bengio
Voraussetzungen / BesonderesThis is an advanced grad-level course that requires a background in machine learning. Students are expected to have a solid mathematical foundation, in particular in linear algebra, multivariate calculus, and probability. The course will focus on state-of-the-art research in deep-learning and will not repeat basics of machine learning

Please take note of the following conditions:
1) The number of participants is limited to 200 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge
3) All practical exercises will require basic knowledge of Python and will use libraries such as TensorFlow, scikit-learn and scikit-image. We will provide introductions to TensorFlow and other libraries that are needed but will not provide introductions to basic programming or Python.

The following courses are strongly recommended as prerequisite:
* "Visual Computing" or "Computer Vision"

The course will be assessed by a final written examination in English. No course materials or electronic devices can be used during the examination. Note that the examination will be based on the contents of the lectures, the associated reading materials and the exercises.
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