Teaching — Robert Reischke

Current Term: Summer Term 2026

Statistical Methods for Astrophysics and Cosmology (lecturer) · University of Bonn
Master's course on Bayesian inference, information theory, and statistical methods. Materials at github.com/rreischke/astrostat_2026 · Lecture notes (PDF).

Week 1, 13.04–17.04.2026: Introduction and organisational matters; Motivation for the course and goals (Section 1); Basics of probability theory (Section 2)
Week 2, 20.04–24.04.2026: Basics of probability theory (Section 2); Starting with estimates (Section 3); Exercise Sheet 1 (will be discussed on 27.04.) · Solution Sheet 1
Week 3, 27.04–01.05.2026: Estimates (Section 3); Exercise Sheet 2 (will be discussed on 04.05.) · Solution Sheet 2
Week 4, 04.05–08.05.2026: Maximum likelihood estimation (Section 4, MLE, Fisher matrix); Exercise Sheet 3 (will be discussed on 11.05.) · Solution Sheet 3
Week 5, 11.05–15.05.2026: Maximum likelihood estimation continued (Section 4, confidence intervals, Cramér-Rao bound, chi-squared); For help in sheet 4 follow the guide in Section 4.12 and check this notebook; Exercise Sheet 4 (will be discussed on 18.05.)
Week 6, 18.05–22.05.2026 (dies academicus): Repetition of MLE
Week 7, 25.05–29.05.2026 (Pentecost): No lecture
Week 8, 01.06–05.06.2026: Bayesian inference (Section 5); Exercise Sheet 5 (will be discussed on 08.06.)
Week 9, 08.06–12.06.2026: Bayesian model comparison (end of Section 5); Sampling methods (Section 6); Exercise Sheet 6 (will be discussed on 15.06.) · Solution Sheet 6
Week 10, 15.06–19.06.2026: Markov Chain Monte Carlo, Metropolis Hastings, analysing MCMC, convergence criteria, alternative MCMC methods (Section 6); Exercise Sheet 7 (will be discussed on 22.06.)
Week 11, 22.06–26.06.2026: Alternative MCMC methods, example with linear regression, repetition, summary and questions (Section 6); There will be no entirely new topic this week because I think everyone's brain will be fried enough this week given the heat; MCMC example PageRank from google
Week 12, 29.06–03.07.2026: Machine learning (section 7), definition, loss, regularisation, fitting; Exercise Sheet 8 (will be discussed on 06.07.) · Solution Sheet 8
Week 13, 06.07–10.07.2026: Machine learning (section 7), regularisation, neural networks, training, backpropagation; Exercise Sheet 9 (will be discussed on 13.07.) · Solution Sheet 9
Week 14, 13.07–17.07.2026: Simulation-based inference (section 8), repetition, questions

Selected Previous Courses

2025 Cosmology (head tutor) · University of Bonn
2024, 2025 Theoretical Mechanics (co-lecturer) · University of Bonn
Bachelor's course on Newtonian, Lagrangian, and Hamiltonian mechanics.
2024 Statistical Methods in Cosmology and Astrophysics (co-lecturer) · University of Bonn
Master's course on Bayesian inference, information theory, and statistical methods.
2023 Introductory Course in Astrophysics (co-lecturer) · Ruhr-Universität Bochum
Cosmology part of the introductory course for Bachelor's and Master's students.
2017 General Relativity (tutor) · University of Heidelberg
Leading a weekly exercise group accompanying a Master's course.
2016 General Relativity (head tutor) · University of Heidelberg
Conceptualising exercise sheets, teaching and organising tutors for a Master's course together with Björn Malte Schäfer.
2015 Cosmology (head tutor) · University of Heidelberg
Conceptualising exercise sheets, teaching and organising tutors for a Master's course together with Björn Malte Schäfer.
2014 General Relativity (tutor) · University of Heidelberg
Leading a weekly exercise group accompanying a Master's course.

Selected Summer Schools

2026 The physics of musical instruments (lecturer) · German Academic Scholarship Foundation, Neubeuern
2023 Cosmology (lecturer) · Evangelisches Studienwerk Villigst, Germany
2022 Paradoxes in Physics (lecturer) · German Academic Scholarship Foundation, Neubeuern
2021 Extreme Phenomena in Physics (lecturer) · Hessian Student Academy, Burg Fürsteneck
2018 Structure of the Laws of Nature (lecturer) · German Academic Scholarship Foundation, Greifswald