Concept

Mean absolute percentage error

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Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

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

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

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

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

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

Explores weighting windows, Gibbs phenomenon, Fourier series, and frequency limitation in signals and systems.

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

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

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

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

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

Covers polynomial interpolation and least-squares approximations to minimize errors.

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

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

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 the evolution of real estate prices, hedonic models, and the UBS Swiss Real Estate Bubble Index.

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

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