Ce Zhang: Catalogue data in Autumn Semester 2020
|Name||Dr. Ce Zhang|
Institut für Computing Platforms
ETH Zürich, STF K 513
|252-0817-00L||Distributed Systems Laboratory|
This only applies to Study Regulations 09: In the Master Programme max.10 credits can be accounted by Labs on top of the Interfocus Courses. These Labs will only count towards the Master Programme. Additional Labs will be listed on the Addendum.
|10 credits||9P||G. Alonso, T. Hoefler, A. Klimovic, T. Roscoe, A. Singla, R. Wattenhofer, C. Zhang|
|Abstract||This 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 wireless networks, ad-hoc networks, RFID, and distributed applications on smartphones.|
|Objective||Gain hands-on-experience with real products and the latest technology in distributed systems.|
|Content||This 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 smartphones. The goal 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.|
|263-3300-00L||Data Science Lab |
Only for Data Science MSc.
|14 credits||9P||C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang|
|Abstract||In this class, we bring together data science applications|
provided by ETH researchers outside computer science and
teams of computer science master's students. Two to three
students will form a team working on data science/machine
learning-related research topics provided by scientists in
a diverse range of domains such as astronomy, biology,
social sciences etc.
|Objective||The goal of this class if for students to gain experience|
of dealing with data science and machine learning applications
"in the wild". Students are expected to go through the full
process starting from data cleaning, modeling, execution,
debugging, error analysis, and quality/performance refinement.
|Prerequisites / Notice||Prerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing.|
|263-3504-00L||Hardware Acceleration for Data Processing |
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 credits||2S||G. Alonso, A. Klimovic, C. Zhang|
|Abstract||The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.|
|Objective||The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.|
|Content||The general application areas are big data and machine learning. The systems covered will include systems from computer architecture, high performance computing, data appliances, and data centers.|
|Prerequisites / Notice||Students taking this seminar should have the necessary background in systems and low level programming.|