Optimal Linear Response: Stochastic Dynamical Systems

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Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Covers the theory and applications of optimization with constraints, including key concepts and numerical methods.

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

Covers the application of numerical methods to solve differential equations using MATLAB.

Covers the stability analysis of ODEs using numerical methods and discusses stability conditions.

Covers the numerical resolution of a Cauchy problem using separation of variables and discusses the conditions for the solution's validity.

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Covers numerical methods for solving boundary value problems using finite difference, FFT, and finite element methods.