  These are lecture notes presented at the online 2021 QUC Winter School on
Energy Frontier hosted by the Korea Institute for Advanced Study. They extend
lectures presented at the 2017 and 2018 CTEQ summer schools and the 2020 Hadron
Collider Physics Summer School hosted by Fermilab. Jets in quantum
chromodynamics (QCD) are motivated from familiar results in classical
electricity and magnetism, through identification of structures that exhibit
approximate scale invariance. From this point, an effective description of QCD
jets is developed from simple assumptions that necessitate all-orders
resummation for obtaining finite results. With machine learning becoming an
increasingly important tool of particle physics, I discuss its utility
exclusively from the biased view for increasing human knowledge. A simple
argument that the likelihood for quark versus gluon discrimination is infrared
and collinear safe is presented as an example of this approach. End-of-lecture
exercises are also provided.
