Related lectures (208)
Mean-Square-Error Inference
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Bayesian Parameter Estimation
Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.
Maximum Likelihood: Estimation and Inference
Introduces maximum likelihood estimation, discussing its properties and applications in statistical analysis.
SVMs and Feature Maps
Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.
Optimality and Asymptotics
Explores the optimality of the Least Squares Estimator and its large sample distribution.
Distribution Estimation
Covers the concept of distribution estimation and the optimization of parameters using different estimators.
Estimation Methods: Bias-Variance Tradeoff
Explores the MSE quality measure for estimators and the bias-variance tradeoff.
Diffusion in Fluid Mechanics
Explores diffusion in fluid mechanics, discussing how molecules transport in a fluid without flow and deriving the convection-diffusion equation.
Estimator of Variance
Explores variance estimation, creating personal estimators, correcting bias, and understanding Mean Square Error in statistical analysis.
Stochastic Blockmodel Estimation
Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

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