Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Gustavo Alonso: Catalogue data in Spring Semester 2020

Name Prof. Dr. Gustavo Alonso
FieldInformatik
Address
Institut für Computing Platforms
ETH Zürich, STF J 420
Stampfenbachstrasse 114
8092 Zürich
SWITZERLAND
Telephone+41 44 632 73 06
E-mailalonso@inf.ethz.ch
URLhttp://www.inf.ethz.ch/~alonso
DepartmentComputer Science
RelationshipFull Professor

NumberTitleECTSHoursLecturers
252-0817-00LDistributed Systems Laboratory Information
In the Master Programme max. 10 credits can be accounted by Labs
on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
10 credits9PG. Alonso, T. Hoefler, F. Mattern, A. Singla, R. Wattenhofer, C. Zhang
AbstractThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones.
ObjectiveStudents acquire practical knowledge about technologies from the area of distributed systems.
ContentThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. The objecte of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.
For information of the course or projects available, please contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso.
252-3510-00LComputing Platforms Information Restricted registration - show details
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SG. Alonso, M. J. Giardino, I. Müller, C. Zhang
AbstractThe seminar covers core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems.
ObjectiveThe seminar will cover core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems. The focus will be on fundamental ideas that apply across systems and application areas but with an emphasis on those ideas that apply to cloud platforms and hardware acceler
ContentThe seminar will consist on student presentations based on a list of papers that will be provided at the beginning of the course. Presentations will be done in teams. Presentations will be arranged in slots of 30 minutes talk plus 15 minutes questions. Grades will be assigned based on quality of the presentation, coverage of the topic including material not in the original papers, participation during the seminar, and ability to understand, present, and criticize the underlying technology.
263-3840-00LHardware Architectures for Machine Learning Information
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SG. Alonso, T. Hoefler, C. Zhang
AbstractThe seminar covers recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers.
ObjectiveThe seminar aims at students interested in the system aspects of machine learning, who are willing to bridge the gap across traditional disciplines: machine learning, databases, systems, and computer architecture.
ContentThe seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers.
Prerequisites / NoticeThe seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics.