Lectures related to Derivation of EM for the GMM

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explains the E-step and M-step calculations in the Gaussian Mixture Model, including the pseudocode of the EM algorithm.

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

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Covers the properties of the exponential family and the estimation of parameters.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Explains decision by maximum likelihood and the use of logarithms.

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

Covers random matrix theory and the replica method to compute quantities related to non-hermitic series using the Stieltjes transform.

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

Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Covers logistic regression and maximum likelihood estimation in depth.

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

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

Covers parameter estimation in quantum metrology, Quantum Fisher Information, Ramsey interferometry, entanglement, and noise impact on NooN states.

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.