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Your supervisor said “use machine learning.” Start here.
Get the free Module 1 workbook and a one-page Roadmap — the path from where you are now to a result your collaboration will trust.
Two files, straight to your inbox
Module 1 Workbook
The actual first module of the course. ML ideas mapped to physics you already own — your observables are the inputs, your chi-squared fit is already a model.
The Roadmap (one page)
Six stages from where you are now to a defensible result, plus the five mistakes reviewers catch every time.
Sound familiar?
Your supervisor said "use machine learning in your analysis." You have spent weeks across 15 GitHub repos, three tool tutorials, and a 200-page manual. Your results still look wrong, and the review committee meets in a few months.
You do not need another lecture recording or another scattered notebook. You need the path — in order — and someone to name the pitfalls before you fall into them.
The science is the proof
Machine learning is not a fashion. On the kind of signal-versus-background problems physicists face every day, it keeps far more of the signal you care about while throwing away more of the background — a large, measurable gain over hand-tuned cuts (Roe et al., NIM A 543 (2005) 577–584). The same methods have topped open physics challenges, including the Higgs Machine Learning Challenge, where thousands of teams competed to pull a real signal out of simulated LHC data (Chen & Guestrin, KDD 2016; Adam-Bourdarios et al., JMLR W&CP 42 (2015)).
Start with the free Module 1 and the Roadmap. If it clicks — and for most physicists it does — you will know exactly where to go next.
Get your free copy now
Ready to go the whole way?
The free Module 1 gets you oriented. The complete course walks all six stages end-to-end — from first principles to a result your review committee signs off on.
See the Complete Course — $97 →