Suchergebnis: Katalogdaten im Frühjahrssemester 2019
|Vertiefung in Distributed Systems|
|Wahlfächer der Vertiefung in Distributed Systems|
|252-0312-00L||Ubiquitous Computing||W||3 KP||2V||F. Mattern, S. Mayer|
|Kurzbeschreibung||Ubiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact.|
|Lernziel||The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact.|
|Skript||Copies of slides will be made available|
|Literatur||Will 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
|252-0817-00L||Distributed Systems Laboratory |
Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.
|W||10 KP||9P||G. Alonso, T. Hoefler, F. Mattern, T. Roscoe, A. Singla, R. Wattenhofer, C. Zhang|
|Kurzbeschreibung||Entwicklung 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.|
|Lernziel||Erwerb praktischer Kenntnisse bei Entwicklung und / oder Evaluation eines umfangreicheren praktischen Systems mit Technologien aus dem Gebiet der verteilten Systeme.|
|Inhalt||Entwicklung 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.|
|263-3501-00L||Future Internet |
Previously called Advanced Computer Networks
|W||6 KP||1V + 1U + 3A||A. Singla|
|Kurzbeschreibung||This 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.|
|Lernziel||The 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.|
|Inhalt||The 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.
|Skript||Lecture slides will be made available at the course Web site: https://ndal.ethz.ch/courses/fi.html|
|Literatur||No 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 / Besonderes||An 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.|
|263-3710-00L||Machine Perception |
Number of participants limited to 150.
|W||5 KP||2V + 1U + 1A||O. Hilliges|
|Kurzbeschreibung||Recent 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.|
|Lernziel||Students 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.
|Inhalt||We 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
Book by Ian Goodfellow and Yoshua Bengio
|Voraussetzungen / Besonderes||This 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 150 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.
|263-3826-00L||Data Stream Processing and Analytics||W||6 KP||2V + 2U + 1A||V. Kalavri|
|Kurzbeschreibung||The course covers fundamentals of large-scale data stream processing. The focus is on the design and architecture of modern distributed streaming systems as well as algorithms for analyzing data streams.|
|Lernziel||This course has the goal of providing an overview of the data stream processing model and introducing modern platforms and tools for anlayzing massive data streams. By the end of the course, students should be able to use techniques for extracting knowledge from continuous, fast data streams. They will also have gained a deep understanding of the design and implementation of modern distributed stream processors through a series of hands-on exercises.|
|Inhalt||Modern data-driven applications require continuous, low-latency processing of large-scale, rapid data events such as videos, images, emails, chats, clicks, search queries, financial transactions, traffic records, sensor measurements, etc. Extracting knowledge from these data streams is particularly challenging due to their high speed and massive volume. |
Distributed stream processing has recently become highly popular across industry and academia due to its capabilities to both improve established data processing tasks and to facilitate novel applications with real-time requirements. In this course, we will study the design and architecture of modern distributed streaming systems as well as fundamental algorithms for analyzing data streams.
|Skript||Schedule and lecture notes will be posted in the course website: https://www.systems.ethz.ch/courses/spring2019/dspa/|
|Voraussetzungen / Besonderes||The exercise sessions will be a mixture of (1) reviews, discussions, and evaluation of research papers on data stream processing, and (2) programming assignments on implementing data stream mining algorithms and anlysis tasks. |
- Basic knowledge of relational data management and distributed systems.
- Basic programming skills in Java and/or Rust is necessary to carry out the practical exercises and final project.
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