Y

Related lectures (15)

Covers the Runge-Kutta method for approximating solutions of differential equations.

Covers quantum eigenfunctions and the importance of A and B commuting for the same set of eigenfunctions.

Explores Kolmogorov's 0-1 law, convergence of random variables, tightness, and characteristic functions.

Covers Borel-Cantelli lemmas, laws of random variables, and convergence in probability.

Explores orthogonal bases in linear algebra and their application in solving linear equations.

Explores Taylor polynomials for multivariable functions and their applications in function approximation.

Explores the Stern-Gerlach experiment, strong measurements, cavity quantum electrodynamics, and quantum optomechanics.

Covers measure spaces, integration, Radon-Nikodym property, and inequalities like Jensen, Hölder, and Minkowski.

Explores monotonicity criteria, L'Hopital's rule, and Lipschitz continuity in differentiable functions and deep neural networks.

Covers advanced mathematical concepts used in physics calculations, including multiplication and diagonalization.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Covers ODE RK schemes, including explicit RK stages and accuracy computation.

Covers the background on symplectic geometry, focusing on symplectic manifolds and canonical structures.

Explores laminar shear flow between parallel plates for incompressible fluids.

Explores toy models, sigma-algebras, T-valued random variables, measures, and independence in probability theory.

Page 1 of 1