Kevin Schawinski: Catalogue data in Autumn Semester 2017
|Name||Dr. Kevin Schawinski|
|263-3300-00L||Data Science Lab |
Number of participants limited to 30.
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 credits||9P||C. Zhang, K. Schawinski|
|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||Each student is required to send the lecturer their CV|
and transcript and the lecturer will decide the enrollment
on a per-student basis. Moreover, the students are expected
to have experience about machine learning and deep learning.
EMAIL to send CV: email@example.com
|402-0353-63L||Observational Techniques in Astrophysics||6 credits||2V + 1U||K. Schawinski|
|Abstract||The course introduces analysis techniques, the basics of astronomical instruments, real-world observational tools, data reduction strategy and software packages used in astrophysics research. The course will also include discussions of current topics in astrophysics with a focus on active galaxies. The course will include the reduction and analysis of real data from a variety of observatories.|
|Objective||The goal is to acquaint students with the basics of a range of astrophysical observation techniques including the modern software tools needed to analyze data.|
|Content||Major topics include:|
-Scientific programming and analysis tools
How to set up your computing environment, data management, catalog generation and the Virtual Observatory, collaborative tools
-Optical imaging and spectroscopy:
Basics of observatories (ground vs space), multi-wavelength data, detector types, reduction and analysis strategies for imaging and spectroscopic data, types of spectrographs, interpreting spectra including stellar and galaxy evolution models
-X-ray, IR and radio astronomy
Basics of X-ray and high energy detectors and telescopes, spectral fitting, basics of radio astronomy, interferometric observations, aperture synthesis, source confusion and decomposition
-Planning of observations and proposal writing.
-Analysis of real-world data
Various examples from across the spectrum (ground and space-based)
|Prerequisites / Notice||Astrophysics I is required and Astrophysics II is recommended. Some programming skills in Python or similar languages are necessary.|
|402-0356-00L||Astrophysics Seminar||0 credits||2S||S. Cantalupo, S. Lilly, A. Refregier, K. Schawinski, H. M. Schmid|
|402-0369-00L||Research Colloquium in Astrophysics||0 credits||1K||S. Cantalupo, S. Lilly, A. Refregier, K. Schawinski, H. M. Schmid|
|Abstract||During the semester there is a colloquium every week. In general, colloquia are 20 minutes plus discussion and are given by local researchers. They inform the other members of the Institute of Astronomy about their current work, results, problems and plans. Guests are always welcome.|
|Objective||Ph.D. students are expected to give a first research colloquium within their first years of their graduate time, another colloquium in their third year, and their doctoral exam talk before or after the exam. Other members of the institute are also invited to give talks. The goals are:|
- keep other members of the institute oriented on current research
- test new ideas within the institute before going outside
- train students to give scientific talks