Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.
Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.
Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.
