John Hull, a specialist in derivatives pricing, introduces machine learning for finance in this MIT OpenCourseWare lecture from 18.642, Topics in Mathematics with Applications in Finance. He traces his own path from traditional mathematics into machine learning and frames it as central to a new industrial revolution reshaping financial practice. The lecture covers the three core paradigms, supervised, unsupervised, and reinforcement learning, and works through concrete applications including neural networks used for option pricing and reinforcement learning applied to hedging decisions. Hull connects the technical material to practical payoffs, showing how these methods can lower transaction costs and sharpen risk management in trading desks. The session balances theoretical setup with worked examples, aimed at students who already have a mathematics background but are new to machine learning's role in quantitative finance.