A few years ago, learning robot learning meant stitching together dozens of papers and courses — with no clear path from the basics to what state-of-the-art systems actually do. This was one of the motivations behind creating @ETH's course "Robot Learning: From Fundamentals to Foundation Models", to provide a structured path from first principles all the way to modern foundation models for robotics. I strongly believe that education should be accessible to everyone, so I have made all lecture recordings publicly available on YouTube. Creating this course was one of the most challenging projects I have taken on. It was my first time designing and teaching an entire curriculum from scratch, while simultaneously working full-time in industry. On top of that, the course proved to be more popular than expected and we had to scale it to almost 300 students, which was only possible thanks to an amazing team of TAs. Looking back, it was an absolute privilege to teach this class and an incredibly rewarding experience. If you are getting into robot learning, this is the starting point I wish I had. 📚 Main lectures: youtube.com/watch?v=X0k14u6p… 🎤 Guest lectures: youtube.com/watch?v=aG8NPTPh… 🌐 Course website: cvg.ethz.ch/lectures/Robot-L…

📣 𝗧𝗵𝗿𝗶𝗹𝗹𝗲𝗱 𝘁𝗼 𝗵𝗼𝘀𝘁 𝗗𝗶𝗲𝘁𝗲𝗿 𝗙𝗼𝘅 (@fox_dieter17849) 𝗳𝗼𝗿 𝗮 𝗴𝘂𝗲𝘀𝘁 𝗹𝗲𝗰𝘁𝘂𝗿𝗲 𝗮𝘁 𝘁𝗼𝗱𝗮𝘆'𝘀 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝗼𝗳 𝗥𝗼𝗯𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗙𝗿𝗼𝗺 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝘁𝗼 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝘁 @ETH! Today's speaker almost needs no introduction. Dieter is Professor at the @UW and Senior Research Director at @allen_ai, co-author of the landmark textbook 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀, Fellow of @IEEEorg, @RealAAAI, and @TheOfficialACM, and recipient of the 𝟮𝟬𝟮𝟬 𝗜𝗘𝗘𝗘 𝗣𝗶𝗼𝗻𝗲𝗲𝗿 𝗶𝗻 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗔𝘄𝗮𝗿𝗱 and the 𝟮𝟬𝟮𝟯 𝗜𝗝𝗖𝗔𝗜 𝗝𝗼𝗵𝗻 𝗠𝗰𝗖𝗮𝗿𝘁𝗵𝘆 𝗔𝘄𝗮𝗿𝗱. Today he will be talking about: "𝗧𝗼𝘄𝗮𝗿𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝘁 𝗥𝗼𝗯𝗼𝘁𝘀" 🤖 His talk will cover large-scale data generation for robot manipulation, the role of simulation and sim-to-real transfer, and ongoing work at Ai2 on training models that are genuinely competent across a broad set of tasks — moving beyond narrowly trained, disconnected skills 🚀 If you are in Zürich and want to join us: 📍 Where: Room NO C 60, Sonneggstrasse 5 🕓 When: Today, May 18, 17:00 – 18:00 See you there!

🚀MIT Flow Matching and Diffusion Lecture 2026 Released (diffusion.csail.mit.edu/)! We just released our new MIT 2026 course on flow matching and diffusion models! We teach the full stack of modern AI image, video, protein generators - theory and practice. We include: 📺 Videos: Step-by-step derivations. 📝 Notes: Mathematically self-contained lecture notes 💻 Coding: Hands-on exercises for every component We fully improved last years’ iteration and added new topics: latent spaces, diffusion transformers, building language models with discrete diffusion models. Everything is available here: diffusion.csail.mit.edu/ A huge thanks to Tommi Jaakkola for his support in making this class possible and Ashay Athalye (MIT SOUL) for the incredible production! Was fun to do this with @RShprints! #MachineLearning #GenerativeAI #MIT #DiffusionModels #AI

Very pleased by the recent work done by @hug_nicolas to speed-up our pure Python @numba_jit prototype implementation of gradient boosted trees: github.com/ogrisel/pygbm The master branch is now competitive with LightGBM on the Higgs boson benchmark dataset.

Announcing IMPAC: an IMaging-PsychiAtry Challenge, using data-science to predict autism from brain imaging paris-saclay-cds.github.io/a… 9000€ of prizes to win! More than 2000 individuals scanned! Organized by @SaclayCDS and @R3RT0's team at @institutpasteur

Don’t compare percentage change on a linear scale; use a log scale instead. -50% (0.5×) is as big a change as 100% (2×).

More effective convergence guarantees with AMSGrad, best paper award @ICLR18. Thanks to a slight change in ADAM we can get a stronger update rule for Gradient Descent methods. openreview.net/pdf?id=ryQu7f… #dlearn #Optimization

Maybe now I will remember

Tomorrow I'll actually teach my first ever lecture on deep learning, rebuilding all the basic constructs, starting from LDA up to deep rectified networks. Lecture slides available at montefiore.ulg.ac.be/~geurts…

Worry, if you must, about the future. But you can take comfort in knowing there’s less of it every day.

The hype of being an AI research scientist nowadays.

. @ContinuumIO we have changed our name to Anaconda, Inc. We are more committed than ever to community: anaconda.com/

I am proud to announce TVM, our new deep learning compiler toolchain news.cs.washington.edu/2017/… tvmlang.org/2017/08/17/tvm-r… @guestrin @uwcse

We just released scikit-learn 0.19. Update with pip or conda. A big thank you to everybody who contributed! scikit-learn.org/0.19/whats_…

Nice example of time-series averaging using soft-DTW tslearn.readthedocs.io/en/la… Part of tslearn by Romain Tavenard

New post: Random-Walk #Bayesian Deep Networks: Dealing with Non-Stationary Data twiecki.github.io/blog/2017/… With animations! #PyMC3

ELI5: debug and explain the behavior of Python Machine Learning models: github.com/TeamHG-Memex/eli5… (sklearn, xgboost, lightning, crfsuite....)

Stochastic online matrix factorization in cython with sklearn compatible API by @arthurmensch: github.com/arthurmensch/modl dict learning & nmf

New post: Native Hadoop file system (HDFS) connectivity in Python wesmckinney.com/blog/python-… #pydata