STRUCTURED — SELF-PACED — EVIDENCE-BASED
The Machine-Learning Analysis You Meant to Build — Finished, and Defensible
Six plain-English modules. From your first question about machine learning to a result your collaboration will trust. The structured path that scattered GitHub notebooks cannot give you.
Your supervisor asked you to 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. The review committee meets in four months.
You do not need another lecture recording. You need a structured method — one that takes you from 'where do I even start' to 'here is my finished, defensible result' in a clear sequence.
The Research
On the kind of signal-versus-background problems physicists face every day, machine learning 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)
What You Get
From First Principles
Module 1 builds the machine-learning vocabulary from scratch. No assumed knowledge beyond undergraduate statistics and a little Python.
Real Physics Data
Work with actual collision data and simulation — not toy datasets.
Defensible in Review
Module 6 covers the checks, the memorisation traps, and the systematic uncertainties that review committees actually scrutinize.
Inside the Program
Module 1: Machine Learning for Physicists
Machine-learning words mapped to physics you already know. The too-simple / too-complex balance, training versus testing, and why your chi-squared fit is already a model.
Module 2: Finding Signal in Many Variables at Once
Why hand-tuned cuts hit a wall when you have many variables. Building inputs from detector observables. How machine learning separates signal from background.
Module 3: How the Method Makes Decisions
How a model learns a rule from data, step by step, on real physics data — so it is never a black box you cannot explain.
Module 4: Making the Method Stronger
How combining many simple learners into one strong one lifts your result — and the handful of settings that actually matter.
Module 5: A Full Analysis, Start to Finish
The end-to-end pipeline on a real collider analysis: simulation corrections, reweighting, the practical quirks, and getting from a trained model to a result your convener trusts.
Module 6: Checking Your Work Before Anyone Else Does
Spotting a model that memorised its training data, cross-checking, data-versus-simulation agreement, tuning your selection honestly, and carrying systematic uncertainties through. What makes an analysis publishable.
Before & After
| Before | After |
|---|---|
| 40 hours across scattered GitHub notebooks | 6 structured modules with clear progression |
| Results look wrong, no idea why | A result you understand end to end, with the checks done |
| Figure it out from a 200-page manual | Step-by-step from first principles to a finished analysis |
Who This Is For
- Graduate students asked to use machine learning in an analysis with no roadmap
- Experimental physicists moving beyond hand-tuned cuts to find the signal they keep missing
- Anyone who can do the maths but has never seen the machine-learning workflow end to end
- Computer scientists looking for novel algorithm research — this is applied physics practice
- People wanting a no-maths, no-code overview — you will write a little Python and read your own results
- Anyone expecting a certificate or university credit — this is self-paced training, not accreditation
Everything Included
- Six structured video/audio modules
From the machine-learning vocabulary to a full collider analysis, in the order you would actually build one. - Six exercise workbooks (PDF)
One per module, so you practise on real physics data instead of just watching. - Narrated audio for every module
Listen through the reasoning on your commute or at the whiteboard. - Lifetime access + future updates
Buy once. Revisit any module when your next analysis needs it.
30-Day Guarantee
If within 30 days you feel the course has not improved your ability to use machine learning in your physics analysis, email us for a full refund. No hoops, no drama.
Common Questions
Do I need a machine-learning background?
No. Module 1 builds the vocabulary from undergraduate statistics and Python. If you can run a chi-squared fit, you can start.
Which experiments does this apply to?
The methods are general to collider physics. Worked examples use an LHCb-style pipeline, but the checking and systematics workflow applies at ATLAS, CMS, and beyond.
Is this accredited or certified?
No. This is self-paced professional training, not a university course. It teaches the method; it does not issue credit or a certificate.
What if it is not right for me?
You are covered by our 30-day money-back guarantee. See the guarantee page for the full terms.
How long does it take to complete?
About 7 hours of audio/video across six modules, plus the workbooks. Most people work through it in 2-4 weeks alongside their analysis, but there is no deadline — you have lifetime access.
Can I access it on my phone or tablet?
Yes. The audio modules stream in any browser and the PDF workbooks open on any device. No app required.
What if I already know some of this?
Skip what you know. The modules are sequential but standalone — if you are already comfortable with the basics, jump straight to Module 4 or Module 5 (the full analysis, start to finish).