Inference & machine learning notes

These are short notes on statistical inference and machine learning concepts:

1. probability: a review of basic probability with a few worked-out examples.

2. hmms: a brief introduction to Hidden Markov Models and the forward-backward algorithm, with a fully worked-out example.

3. ising-gibbs: a brief introduction to Markov chain Monte Carlo (MCMC) methods, including Metropolis-Hastings and Gibbs sampling. Also covers the Ising model, commonly used in applications like computer vision.

4. gmm-em: a brief introduction to Gaussian mixture models (useful for modeling continuous data from several different groups) and the EM algorithm.

You can find the notes themselves at my website.

Compiling

Run make from the top directory to compile everything, or to compile an individual document, e.g., hmms, run make hmms.

Licensing

Copyright (C) 2013 Ramesh Sridharan, rameshvs@csail.mit.edu.

This work is licensed under a Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA 4.0) license.