Lectures related to Maximum Likelihood Inference

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

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Covers the basics of probability theory, random variables, joint probability, and inference.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

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

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

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

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

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

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

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

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

Covers the concepts of Demystification, Estimation, and Bayesian Inference in the context of Bayesian statistics.

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

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.