Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.
Explores filtering signals with a moving average filter and the process of sampling, emphasizing the importance of signal reconstruction from samples.
Explains the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for sampling.
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
