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.

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20+ peer-reviewed studiesSelf-paced audio + workbooksReal collision data

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.

Peer-reviewed evidence

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

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

6 Modules

Module 1: Machine Learning for PhysicistsMachine-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 OnceWhy 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 DecisionsHow 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 StrongerHow 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 FinishThe 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 DoesSpotting 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

BeforeAfter
40 hours across scattered GitHub notebooks6 structured modules with clear progression
Results look wrong, no idea whyA result you understand end to end, with the checks done
Figure it out from a 200-page manualStep-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

What you are getting

Everything Included

  • Six structured video/audio modulesFrom 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 moduleListen through the reasoning on your commute or at the whiteboard.
  • Lifetime access + future updatesBuy once. Revisit any module when your next analysis needs it.

30-day money-back 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.

Full guarantee details →

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).

Full scientific bibliography (20 peer-reviewed studies)

Every claim in this course traces back to published, peer-reviewed research.

  1. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
  2. Freund, Y., & Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119-139.
  3. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189-1232.
  4. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. arXiv:1603.02754.
  5. Roe, B. P., Yang, H.-J., Zhu, J., Liu, Y., Stancu, I., & McGregor, G. (2005). Boosted decision trees as an alternative to artificial neural networks for particle identification. Nuclear Instruments and Methods in Physics Research A, 543, 577-584. arXiv:physics/0408124.
  6. Hoecker, A., Speckmayer, P., Stelzer, J., Therhaag, J., von Toerne, E., & Voss, H. (2007). TMVA - Toolkit for Multivariate Data Analysis. PoS ACAT, 040. arXiv:physics/0703039.
  7. Baldi, P., Sadowski, P., & Whiteson, D. (2014). Searching for exotic particles in high-energy physics with deep learning. Nature Communications, 5, 4308. arXiv:1402.4735.
  8. Radovic, A., Williams, M., Rousseau, D., Kagan, M., Bonacorsi, D., Himmel, A., Aurisano, A., Terao, K., & Wongjirad, T. (2018). Machine learning at the energy and intensity frontiers of particle physics. Nature, 560, 41-48.
  9. Guest, D., Cranmer, K., & Whiteson, D. (2018). Deep Learning and its Application to LHC Physics. Annual Review of Nuclear and Particle Science, 68, 161-181. arXiv:1806.11484.
  10. Albertsson, K., et al. (2018). Machine Learning in High Energy Physics Community White Paper. Journal of Physics: Conference Series, 1085, 022008. arXiv:1807.02876.
  11. Bourilkov, D. (2019). Machine and Deep Learning Applications in Particle Physics. International Journal of Modern Physics A, 34, 1930019. arXiv:1912.08245.
  12. de Oliveira, L., Kagan, M., Mackey, L., Nachman, B., & Schwartzman, A. (2016). Jet-images - deep learning edition. Journal of High Energy Physics, 2016(7), 69. arXiv:1511.05190.
  13. Komiske, P. T., Metodiev, E. M., & Thaler, J. (2019). Energy Flow Networks: Deep Sets for Particle Jets. Journal of High Energy Physics, 2019(1), 121. arXiv:1810.05165.
  14. Qu, H., & Gouskos, L. (2020). ParticleNet: Jet Tagging via Particle Clouds. Physical Review D, 101, 056019. arXiv:1902.08570.
  15. Kasieczka, G., et al. (2019). The Machine Learning Landscape of Top Taggers. SciPost Physics, 7, 014. arXiv:1902.09914.
  16. Louppe, G., Kagan, M., & Cranmer, K. (2017). Learning to Pivot with Adversarial Networks. Advances in Neural Information Processing Systems, 30. arXiv:1611.01046.
  17. Shimmin, C., Sadowski, P., Baldi, P., Weik, E., Whiteson, D., Goul, E., & Sogaard, A. (2017). Decorrelated Jet Substructure Tagging using Adversarial Neural Networks. Physical Review D, 96, 074034. arXiv:1703.03507.
  18. Cranmer, K., Pavez, J., & Louppe, G. (2015). Approximating Likelihood Ratios with Calibrated Discriminative Classifiers. arXiv:1506.02169.
  19. Cranmer, K., Brehmer, J., & Louppe, G. (2020). The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117(48), 30055-30062. arXiv:1911.01429.
  20. Paganini, M., de Oliveira, L., & Nachman, B. (2018). CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. Physical Review D, 97, 014021. arXiv:1712.10321.

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