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The 2023 Summer School for Astrostatistics in Crete – the announcement
Here comes the announcement of the third (already!) iteration of our Summer School for Astrostatistics in Crete!
This is an in-person meeting, as it focus on the practical use of statistics and machine learning in academic research. We will supply all the necessary guidelines through Astronomical problems.
Check the website for further information. Registration closes on March 24!
The Astrostatistics Summer School 2022 in Crete – the announcement
Following the success of the first Astrostatistics Summer School in Crete in 2019 (18-21 June), we now organize its second iteration (11-15 July). Initially scheduled for 2020, but we all now what happened and it was delayed. Now things to have matured so that we can actually repeat it and of course with an in person meeting as this school presents the theory but it is heavily based on actual hands-on experience with real astronomical data.
For all interested individuals please check the website. There is not much time before we meet in July !
Astrostatistics Summer School Crete 2019 – last day
During the last four days I was busy with the actual materialization of the Astrostatistics Summer School Crete 2019, which took place at the University of Crete, in Heraklion (18-21 June 2019). My duties were mainly those of the Teaching Assistant and I contributed with a short presentation of the Random Forests method for classification. Unfortunately I didn’t have the time to post more on this school, so I ended up doing something only today, at the very last day!
In summary this is/was a school for graduate and early-stage post-docs to get a grasp of the modern field of Astrostatistics, which practically means the application of statistics in Astronomy which incorporates also machine-learning techniques. Topics include: Intro to Python and Jupyter notebook, linear regression, classical statistical distribution tests and hypothesis testing, Bayesian statistics, Markov-Chain Monte Carlo, machine learning classification/regression/clustering, time series analysis.
The school is split in “teaching”/explanatory parts (through Jupyter notebooks though where you could also interact and run the examples) and practical workshops, where important hands-on experience with all the tools presented was provided (and hopefully gained!). However, I think the importance lies in the fact that all the material is publicly available through a github repository: https://github.com/astrostatistics-in-crete (including the notebooks with the introduction notes, the exercises in the workshops, and the data). So this is a valuable source both for students of the school, as well as others interested to try and experiment with these tools.
