Densities and Bayesian Inference: Decision Rules and Bayes Law

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Statistical Inference: Exponential Families and Likelihoods

Explores exponential families, likelihood functions, and model regularity in statistical inference.

Extreme Value Analysis: Statistical Consequences

Explores the statistical consequences of extreme value analysis and implications of model mis-specification.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Bayesian Statistics: Hypothesis Testing and Estimation

Covers hypothesis testing, p-values, significance levels, and Bayesian estimation.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Model Selection: AIC and BIC

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

Discrete Choice Analysis

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

Bayesian Inference: Beta-Bernoulli Model

Explores the Beta distribution, Bayesian inference, and posterior calculation in the Beta-Bernoulli model.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.