Stochastic Processes
Graduate-level course at UofT and a core research interest. Probability theory applied to systems that evolve randomly over time.
Level: Graduate-level coursework · core research focus
Topics covered
- Markov chains (discrete and continuous time), stationary distributions
- Brownian motion and Wiener processes
- Poisson processes and renewal theory
- Martingales and optional stopping
- Itô calculus (introductory)
Why it matters
ML relies on stochastic processes throughout: Markov decision processes (RL), stochastic gradient descent, diffusion models, probabilistic graphical models. Statistics and Physics (statistical mechanics, diffusion, Langevin dynamics) are deeply connected to this field.
Related: Mathematics, Statistics, ML, Physics