The purpose of this course is to provide an hands-on introduction on foundations of probability as well as an insight into the field of Data Analysis with large sets of experimental data. The students will learn how to use and understand basic tools and methods which are used by researchers involved in searches for astrophysical signals in both the electromagnetic and gravitational spectrum.
**Join on [STUD IP](https://studip.uni-hannover.de/dispatch.php/course/overview?cid=029d8a01f7d61e418cfd77ef6466270d)**
**Join on [STUD IP](https://studip.uni-hannover.de/dispatch.php/course/overview?cid=78c7b37a5454b800d82e105c1ac41e47)**
# Table of Contents
[Resources](resources.md)
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@@ -32,24 +32,24 @@ The purpose of this course is to provide an hands-on introduction on foundations
- It is strongly suggested not to miss any class. No more than one class is supposed to be missed.
- No class participation and extra good sheet **is not** a good sign
- Python only
- For **any** problem: just contact me -> gianluca.pagliaro@aei.mpg.de
- For **any** problem: just contact me -> jasper.martins@aei.mpg.de
- Deadlines: TBA
- Final report: put all sheets together. Can’t miss any sheet.
## Schedule
**Tuesdays 15:00 to 18:00 Room 103 or 106, Callinstr 38 (AEI main entrance)**
**Tuesdays 14:00 to 18:00 Room 103 or 106, Callinstr 38 (AEI main entrance)**
This course awards a total of 4 ECTS credits. To each ECTS credit corresponds an average or 30 hours of workload. According to this, and based on our planned weekly schedule, for each hour spent in class, you are (on average) supposed to study 1.5 hours at home.
More infos at: [LEISTUNGSPUNKTE](https://www.uni-hannover.de/de/studium/vor-dem-studium/orientierung-studienentscheidung/studienaufbau#c89762)
***16<sup>th</sup> October 2023** Introduction/First steps
***17<sup>th</sup> October 2023** Introduction/First steps
***24<sup>th</sup> October - 7<sup>th</sup> November 2023** Monte Carlo Methods
***13<sup>th</sup> November 2023** Basic principles of Bayesian inference
***14<sup>th</sup> November 2023** Basic principles of Bayesian inference
***21<sup>st</sup> - 28<sup>th</sup> November 2023** Combinatorics
