We offer a wide range of graduate level courses in general probability and stochastic processes which together form a comprehensive basis for advanced studies in probability.
The courses are augmented, yearly, by advanced special topic courses, as listed below.
- Random permutations – Pinsky
Selection of courses from previous semesters
- Mathematical foundations of deep learning – Louidor
- Interacting particle systems – Procaccia
- Brownian motion and stochastic calculus – Crawford
- The Ising model – Rosenthal.
- Random graphs and complexes – Bobrowski
- Probability on graphs – Rosenthal
- Random graphs – Yeo
- Random matrices – Rosenthal
- Random interface models – Louidor
- Hydrodynamic limits – Crawford
- Universality of exit times for random walks – Wacthel.
- Gaussian processes and geometry – Adler
- Stochastic partial differential equations and measure-valued branching processes – Mytnik
- The discrete Gaussian free field and related topics – Louidor
- Gaussian processes (and some geometry) – Adler
- Long range dependence and heavy tails – Owada
- The theory of random permutations and some connections to probabilistic number theory – Pinsky
- Levy processes and local times of Markov processes – Kaspi
- Markov processes – Kaspi
- Spatial stochastic processes – Adler
- Large scale dynamics of interacting particles – Ioffe
- Directed polymers and random geodesics – Ioffe
- Levy processes – Mytnik
- Topological methods in electrical and systems engineering – Adler