Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores discrete panel data, covering static and dynamic models with panel effects and their practical implications.
Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
