Designed and taught INT 93LS–001: Probabilistic Computing, an intensive UCSB summer course. Students explored Monte Carlo methods, Ising and Potts models, simulated annealing, and stochastic neural networks through lecture notes and executable notebooks.
2026 course description
Advancing Scalable and Energy-Efficient Machine Intelligence
Summer Research Academies · Track 1 · INT 93LS–001
The course examines how randomness can become a useful computing resource as conventional systems encounter limits in power use and physical scaling. Students connect probability, linear algebra, optimization, and machine learning to algorithms designed for efficient computation.
Through Python implementations, students study Markov chain sampling with Gibbs and Metropolis–Hastings methods, then use simulated annealing and parallel tempering to tackle combinatorial optimization. The emphasis is on understanding how stochastic algorithms work and evaluating the computational benefits they can offer.
The course also applies probabilistic methods to neural networks. Students train stochastic models, compare them with deterministic baselines, and examine the trade-offs among accuracy, runtime, and energy use. They leave with practical experience building probabilistic algorithms and assessing when engineered randomness is useful for machine intelligence.
Source: UCSB Summer Sessions track listing, saved November 3, 2026.