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Aitken's delta-squared process
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Numerical Differentiation: Richardson Extrapolation
Covers Richardson extrapolation for numerical differentiation to reduce error.
Numerical Integration: Simpson's Rule
Introduces Simpson's rule for numerical integration and Richardson extrapolation for accuracy improvement.
Splines: Fundamentals and Applications
Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.
Computation, Verification and Validation
Explains verification and validation in CFD simulations, focusing on Richardson extrapolation and accuracy checking.
Error Estimation and Numerical Integration
Explores error estimation in numerical integration and its applications in forecasting, emphasizing the Romberg method and Richardson extrapolation.
Double Descent Curves: Overparametrization
Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.
Universal Approximation Theorem: MLP
Covers Multi-Layer Perceptrons (MLP) and their application from classification to regression, including the Universal Approximation Theorem and challenges with gradients.
Rare Events and Extreme Modeling
Explores rare event modeling, challenges in estimating probabilities with limited data, and the application of extreme value theory in various fields.
Numerical Integration: Lagrange Interpolation, Simpson Rules
Explains Lagrange interpolation for numerical integration and introduces Simpson's rules.
Calculating Average Growth Rates
Explores the calculation of average growth rates and the sensitivity of growth rates to different time periods.
Determinantal Point Processes and Extrapolation
Covers determinantal point processes, sine-process, and their extrapolation in different spaces.
Newton Method: Convergence and Quadratic Care
Covers the Newton method and its convergence properties near the optimal point.
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