__(qft.pdf) -- This document presents some notes on the Higgs mechanism, Quantum Field Theory (QFT), and the Standard Model (SM). The notes aim to provide a big picture understanding of QFT, resorting to the simplest working models to get the points across. Moreover, the notes emphasize the symmetries of nature that essentially build the SM. There aren't any lengthy calculations of decay cross sections here, so you'll have to go to the usual sources for those.__

**Quantum Field Theory**After a brief introduction to the SM, the notes explain the Lagrangian formalism and examine the symmetries of some simple cases in classical mechanics. Next, the Lorentz symmetries are explored along with some basic group theory. The constraints of the Lorentz symmetries are then used to derive the Lagrangians for the particles of the Standard Model. After exploring Lorentz symmetries, the Lagrangian formalism is extended to quantum mechanics and quantum field theory via path integrals. The notes then go on to describe perturbation theory and Feynman diagrams using the simplest possible model (a self interacting scalar particle). Finally, the Lagrangians for the matter particles and the forces are coupled, gauge symmetries are discussed, and the Higgs mechanism is explored. The notes end with a discussion of the Higgs boson at the Large Hadron Collider.

__(stats.pdf)-- A simple coin flipping experiment is used to explain p-values, limits, best fit values, confidence intervals, likelihoods, and likelihood constraints. The concepts are then extended to a typical High Energy Physics (HEP) analysis. The notes are intended for beginning graduate students in HEP, but should be useful for anyone interested in statistical tests and parameter estimation. I try to avoid lengthy calculations that distract from the main ideas.__

**Statistics in High Energy Particle Physics**__(bdts.pdf) -- This document outlines the Boosted Decision Tree (BDT) machine learning algorithm. The text starts with a cursory summary of the full BDT algorithm. After presenting a bird's eye view, the text starts by explaining the decision tree algorithm. After discussing the DT algorithm, the notes move through a concrete example step by step and then provide pseudocode for the DT algorithm. The document then moves on to explain boosting and later presents pseudocode for the full BDT algorithm. This text was originally written as part of a larger document for a project during my PhD at the Large Hadron Collider. I extracted the relevant BDT info and put it into this document in case people find it useful.__

**Boosted Decision Trees**