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The optimization problem have two components that are objective function \(f_0 : \mathbb R ^n \rightarrow \mathbb R\) and the …
Last updated on 1 min read Convex optimization, Math, Machine learning

The purpose of approximation is finding optimal point \(x^*\) i.e. \(\nabla F(x^*) = 0\). We need a step/search direction \(\Delta x\) …
Last updated on 2 min read Math, Machine learning, Convex optimization

Bases are the central idea of linear algebra. An invertable square matrix has eigenvectors. A symetric matrix has orthogonal …
Last updated on 2 min read Math, Machine learning

This is a note for part III of Linear Algebra and learning from data, Gilbert Strang The main themes are sparsity (Low rank), …
Last updated on 2 min read Math, Machine learning

Last updated on 1 min read Math, Machine learning

Differential equations describe the change of state. The change relates to the state. The solutions of the differential equations are …
Last updated on 2 min read Math

Information relates to uncertainty. The Shannon information content of an outcome \(x\) is \(h(x)=-log_{2}P(x)\). The rare event has …
Last updated on 1 min read Math, Information Theory

Last updated on 1 min read R

\[ f(x) = \sum_{k=0}^\infty c_k x^k = c_0 + c_1 x + c_2 x^2 + \dotsb. \] This is an approximation that is a function of h and …
Last updated on 2 min read Math

Last updated on 1 min read R, Tidymodel