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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.