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

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

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.

Covers logistic regression and maximum likelihood estimation in depth.

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

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 Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

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

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.

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.

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