Lectures related to Root-mean-square deviation

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

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

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

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Explores statistical analysis, mean square error, and bootstrapping methods in optimization and simulation.

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

Covers the analysis of GPS data, including true value comparison and deviation analysis.

Explores the mathematical reasoning behind the consistency of maximum likelihood estimation.

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

Explores statistical dispersion and its impact on determining normal values and data analysis.

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

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

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

Covers adaptive signal processing and the LMS filter for iterative minimization and white input signals.

Introduces Fisher Information for parameter estimation based on collected data.

Covers structural modeling, state space models, and the Kalman filter in time series analysis.

Explores the convergence analysis of stochastic gradient algorithms under various operational modes and step-size sequences.