Lectures related to SGD and Mean Field Analysis

Covers brain-computer interfaces and their impact on systems neuroscience and neuroprosthetics.

Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Covers calculating with Taylor series and convexity concepts, including sin(x) and cos(x) approximations.

Explores landmarks, coordinates, vectors, coplanarity, Cartesian equations, and geometric rules in space.

Covers finite horizon reinforcement learning, non-stationary policies, and the optimistic variant of Proximal Policy Optimization (OPPO).