Generative Models: Logistic Regression & Gaussian Distribution

Related lectures (32)

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Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Explores optimal Bayesian inference, denoising, scalar estimation, and phase transitions.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

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

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

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

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

Covers linear models for classification, focusing on logistic regression and support vector machines.

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

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.