Pedagogy

Notes

Long-form notes from courses in physics, mathematics, statistics, and machine learning.

Physics

01Classical mechanicsSelected derivations from Yale’s classical mechanics course.PDF
02ElectrodynamicsHandwritten course notes following Griffiths’ treatment of electrodynamics.PDF
03Quantum mechanicsHandwritten notes from Yale’s two-course quantum mechanics sequence.Quantum IQuantum II
04Statistical mechanics and thermodynamicsA typed reference and separate handwritten notes from Yale’s statistical mechanics course.Typed referenceClass notes
05Complex systemsCourse notes on bifurcations, stability, nonlinear dynamics, and deterministic chaos.PDF

Mathematics, statistics & learning

01Complex analysisA review guide for Yale’s complex analysis course, organized around Gamelin.PDF
02Theory of statisticsA course review of probability, estimation, hypothesis testing, and asymptotic ideas.PDF
03Stochastic processesCourse notes on Markov chains, martingales, Brownian motion, diffusions, and stochastic differential equations.PDF
04Information theoryCourse notes on entropy, coding, typicality, and channels, following Cover and Thomas.PDF
05Convex optimizationCourse notes on convex sets, duality, and optimality, following Boyd and Vandenberghe.PDF
06AlgorithmsHandwritten course notes on algorithms, data structures, and complexity analysis.PDF
07Machine learningCourse notes on statistical learning, neural networks, reinforcement learning, and representation methods.PDF