Introduction to Hidden Markov Models

author: Antonio Artés Rodríguez, Department of Signal Theory and Communications, Carlos III University of Madrid
published: Feb. 17, 2015,   recorded: September 2014,   views: 9742


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: