Stephen covered randomized algorithms for probability.
Specifically, we saw a randomized trace estimator and its applications to least-squares, stochastic gradient descent, and Markov chain Monte Carlo for the Ising model.
The lecture was based on these lecture notes.
Previous: First meeting
For our first meeting of the spring semester, we introduced the club and give a few interesting examples of theoretical computer science.
continue reading ❯