Machine learning PhD student at Uni Tübingen. Thinking about probabilistic numerics, probabilistic ML and scientific computing.
I've open-sourced a bunch of probabilistic numerics code (https://github.com/pnkraemer) and I'm not planning on stopping anytime soon.
| Website | pnkraemer.github.io |
| https://twitter.com/pnkraemer | |
| Github | https://github.com/pnkraemer |
This week we released lectures 5-8 of *Numerics of Machine Learning*, which is the section on **Simulation Methods** (i.e. the solution of differential equations), taught by Jonathan Schmidt, @nathanaelbosch, and Marvin Pförtner
Videos: https://www.youtube.com/playlist?list=PL05umP7R6ij2lwDdj7IkuHoP9vHlEcH0s
Slides: https://github.com/philipphennig/NumericsOfML
In lecture 5, Jonathan introduces state-space models and Bayesian filters (more in thread).
This term, my group is teaching a Master-level lecture course on *Numerics of Machine Learning*. Naturally, from the perspective of #probabilisticnumerics. Featuring established results and new insights, the course is taught primarily by the PhD students - the people closest to the cutting edge.
We are making the course public as material for interested students and lecturers.
Today we're releasing Lectures 1 (Intro) and 2-4, which cover linear algebra. Here are links, and what to expect:
#ProbNumSchool information!
Almost all spots are filled.
Next Friday, 20th of January, we will admit the final batch of applicants to the #ProbNumSchool. (End of the day, German time.) If you apply afterwards, we will place you on a waiting list.
So hurry up and get one of the last spots! Apply here (https://www.probnumschool.org/pages/registration.html#navBar) and come to Tübingen next March to learn about probabilistic numerics and meet a whole bunch of amazing people.
We look forward to hosting you!
If you are following our ELLIS seminar on Advances in Probabilistic #MachineLearning (APML), we are starting off with the first talk of 2023 tomorrow!
Vincent Fortuin (@vincefort) will be talking about the “Importance of Priors in Bayesian Deep Learning”.
See: https://aaltoml.github.io/apml/ for details and make sure to join!
Boosting appreciated.
cc: @FCAI
This seminar series aims to provide a platform for young researchers (PhD student or post-doc level) to give invited talks about their research, intending to have a diverse set of talks & speakers on topics related to probabilistic machine learning.
Thanks to all of you that have signed up for the #ProbNumSchool so far!
**The seats are filling up quickly**. While we still have a few spots left, we admit participants on a rolling basis and once the spots are filled, the application will be closed.
Get your seat here: https://www.probnumschool.org/pages/registration.html#navBar.
Looking forward to seeing you all in Tübingen 🎉
As @pnkraemer announced over on the birdsite, the inaugural Probabilistic Numerics Spring School - probnumschool.org - will take place in Tübingen in late March 2023. I am beyond excited!
Whether you are an interested newbie, or a committed #probnum expert, there’s something here for you: 🧵
I do research on probabilistic numerical algorithms, focusing quite heavily on differential equations. I've open-sourced a bunch of tools for probabilistic numerics and differential equations, most of it in #JAX, and I'm not planning on stopping anytime soon ✌️
Follow me to hear about the new stuff that's happening in the #ProbNum universe and don't be confused by the occasional #matplotlib-post 🎨
Philipp Hennig
unlofl [Promoted Toot]
Martin Trapp
Philipp Hennig