Mean squared prediction error

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Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.

Explores spectral estimation for Gaussian and binary signals in the spiked matrix estimation problem, analyzing the impact of signal-to-noise ratio.

Explores thresholding, ROC curves, regression evaluation, naive methods, and model selection in machine learning.

Explains estimation through mean-squared error and Fisher information in the context of adaptive filters and exponentiated distributions.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.