STRUCTURED. EVIDENCE-BASED. FOR PHYSICISTS.
Build Production-Ready BDTs for Your Analysis
The structured, self-paced course on Boosted Decision Trees and multivariate analysis. From first principles to a validated, publication-ready classifier.
Take the Free AssessmentWhich challenge fits you?
New to ML in HEP
Your supervisor assigned a BDT-based analysis and you don't know where to start
Moving Beyond Cut-Flow
You know rectangular cuts inside out but your analysis needs multivariate methods
Reviewing BDT Analyses
You need to evaluate whether a colleague's BDT analysis is trustworthy enough to publish
How It Works
Take the Assessment
A quick diagnostic identifies whether you need the full course, a quick-start guide, or advanced validation techniques.
Follow the Modules
Six structured modules take you from ML vocabulary through decision trees, boosting, and full LHCb-style analysis pipelines.
Validate and Publish
Learn overtraining diagnostics, k-fold cross-validation, systematic uncertainties, and everything review committees scrutinize.
What the Research Says
BDTs outperform rectangular cuts on multivariate classification tasks in HEP, often improving signal efficiency by 30-50% at equal background rejection.Roe et al., NIM A 543 (2005) 577-584
Gradient boosting (XGBoost/AdaBoost) consistently ranks among top-performing classifiers in HEP benchmarks including the HiggsML challenge.Chen & Guestrin, KDD 2016; Adam-Bourdarios et al., JMLR W&CP 42 (2015)
30-day full refund if the course doesn't meet your expectations
30-day money-back guarantee. No questions asked.
Ready to Build Your First BDT?
Take the free 2-minute assessment and find out which learning path fits your analysis needs.
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