Covers the numerical approximation of PDEs, including Poisson and heat equations, transport phenomena, and incompressible limits.
Covers the basics of algorithms, their historical significance, pseudo-code representation, and practical tasks in computational thinking.
Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.
Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.
Explores Laplacian solvers, covering approximate solutions, applications, error conversion, and theoretical advancements in computational methods.
