Latent Space and RDPG: Statistical Analysis of Network Data

Related lectures (30)

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Explores latent space models, network representations, spectral decompositions, and parameter estimation methods.

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

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Covers the multivariate normal distribution, properties, and sampling methods.

Covers supervised learning through likelihood maximization to find optimal parameters.

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

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

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

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

Explores a new formulation of logistic regression and its connection to empirical risk minimization.