IMPRS course 'Applied statistics & data analysis - Advanced'
- Start: Nov 27, 2023
- End: Dec 1, 2023
1. General information
Date: November 27 - December 1, 2023
Location: MPI-BGC, B0.002
- talks by participants
- excursion talks
Instructors: Martin Jung, Christian Reimers et al.
Category: Skill course
0.2 CP per course day
2. Aims and scope
The course will cover selected topics of advanced statistics and machine learning. Lectures on some topics will be accompanied with presentations by participants, “Excursion” talks on applications in research, and basic practicals in the afternoon. The course requires basic knowledge of statistics. The practical session require basic knowledge with a programming language – examples will be provided in Python.
- Basic knowledge of a language of Python
- Make use of the Python course
- Either the course 'Applied statistics & data analysis - Basics' or recalling the typical “statistics 1” type of lectures from university.
Exercises will be in Python – the use of any other language is welcome; however support depends on the person in charge and cannot be guaranteed.
Bring a laptop with a recent version of Python being installed or running for the practicals. If you prefer another language, that is fine but we will not provide corresponding code examples. Please also make sure that you can access the internet via WLAN (BGC-users, if you have a BGC-account; BGC-guests, if you don't have an account).
3. Presentations by participants (mandatory for assignment)
Participants will give a presentation (15 min + 5 min Q&A) on a paper or topic of their choice. Below you can find a list of suggested papers. If you want to work on a topic in a team of 2 (i.e. 30 min + 10 min Q&A) or suggest an alternative topic please inquire this until 31st October with the proposed topic to email@example.com.
During registration please choose a topic that was not yet chosen.
All presentations need to be ready on Monday, Nov 6th, 2022 at 9 am. The detailed schedule will be announced then.
The presentations should be educational and try to focus on the important things one should know about a method when applying it, i.e. the principle, advantages, disadvantages, assumptions, and pitfalls, rather than all mathematic details, derivations, theorems and proofs. Practical examples are often very illustrative.
Please register here.