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Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics
Angewandte Multi-Messenger-Astronomie 2: Statistische und Machine-Learning-Methoden in Teilchen- und Astrophysik

Course 0000000033 in SS 2020

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 4 SWS
Organisational Unit Experimental Physics with Cosmic Particles
Lecturers Elisa Resconi
Christian Haack
Patrick Vaudrevange
Martin Wolf
Dates Fri, 10:00–13:45, PH 1161

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks Lecture organized in specialized topics following the list: - 26.04.2019, Statistical models and point estimation (by Dr. Matteo Agostini) - 3.05.2019 Frequentist hypothesis testing and sensitivity (by Dr. Matteo Agostini) - 10.05.2019 Frequentist interval estimation (by Dr. Matteo Agostini) - 17.05.2019 Frequentist interval estimation (Part 2) / Introduction to IceCube physics (by Dr. Hans Niederhausen) - 24.05.2019 A statistical model of the IceCube detection process (by Dr. Hans Niederhausen) - 31.05.2019 Searching for a needle in the needle stack: the case of neutrino point sources in the sky (by Dr. Hans Niederhausen) - 7.06.2019 Fitting a line (by Dr. J. Michael Burgess) - 14.06.2019 Fitting a lot of lines (by Dr. J. Michael Burgess) - 21.06.2019 Lines, multiple lines, fitting the JLA data set to determine the cosmological parameters (by Dr. J. Michael Burgess)
Links E-Learning course (e. g. Moodle)
TUMonline entry
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