The lecture will present an overview on Hidden Markov Models (HMM), an ubiquitous tool for dealing with sequential data. We will introduce student different methods for estimating the hidden states and model parameters. We will consider classic as well as parametric and non-parametric Bayesian inference methods, and methods suited for massive data sets like spectral learning.