Likelihoodist statistics

Related lectures (13)

Explains logistic regression, mapping predictions to probabilities and optimizing likelihood.

Explores decision rules based on likelihood ratios and the maximum a posteriori decision.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Covers the maximum likelihood estimator expression and the gradient of the loss function.

Explains Gaussian functions, modeling data, likelihood function, and maximum likelihood optimization.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Covers protein contact prediction using Potts models and pseudolikelihood methods, comparing different approaches for contact prediction in proteins.

Covers supervised learning through likelihood maximization to find optimal parameters.

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

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