In 2010, when the LHC started colliding proton pairs in earnest, multi-variate analyses were newfangled methods starting to make inroads in experimental particle physics. These methods faced widespread skepticism as to their performance and biases, reflecting a winter of suspicion over overtrained neural networks that set in in the late 1990s. Thanks to more robust techniques, like boosted decision trees, it became possible to make better and more extensive use of the full information recorded in particle collisions at the Tevatron and LHC colliders.

The Higgs boson discovery by the CMS and ATLAS collaborations in 2012 was only possible because of the use of multi-variate techniques that enhanced the sensitivity by up to the equivalent of having 50% more collision data available for analysis.

We will review the use of classification and regression in the Higgs to diphoton search and subsequent discovery, a concrete example of a decade-old ML-based analysis in high-energy particle physics. Particular emphasis will be placed in the modular design of the analysis and the inherent explainability advantages, used to great effect in assuaging concerns raised by hundreds of initially-skeptical colleagues in the CMS collaboration.

Finally, we'll quickly highlight some particle physics challenges that have contributed to, and made use of, the last decade of graph, adversarial, and deep ML developments.