Tutorials on the Internet:

High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML) by Roland Speicher; https://www.youtube.com/playlist?list=PLY11JnnnTUCabY4nc0hKptrd5qEWtLoo2 Tutorial on unsupervised feature learning and deep learning by Stanford University: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

Statistical methods for signal processing by Dr. Alfred Hero: http://web.eecs.umich.edu/~hero/Preprints/main_564_08_new.pdf

An introduction to tensors by Dr. Boaz Porat: A gentle introduction to tensors

Resources on reinforcement learning: http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/Resources.html

Slides about book introduction to Machine learning from Dr. Ethem ALPAYDIN (MIT): http://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/

Slides from Dr. Zoubin about unsupervised learning: http://www.cs.nyu.edu/~roweis/notes/nipstut.pdf

Several tutorials, articles, and applications on reinforced learning: http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/Resources.html

Tutorial from Dr. Meir on support vector machine: http://www.ee.technion.ac.il/~rmeir/SVMReview.pdf

Tutorial Presented at ICML-2001 by Dr. Nello Cristianini about support vector machine: http://www.support-vector.net/tutorial.html

Website with plenty of tutorials and information on kernel machines and support vector machines: http://kernel-machines.org/

Slides on MSE minimal bounds presented by Dr. Alexandre Renaux: WUSTL_Alex_MinimalBounds.ppt

Computer vision demonstration website: http://users.ecs.soton.ac.uk/msn/book/new_demo/

On sequential Monte Carlo sampling methods for Bayesian filtering:      http://www.cs.ubc.ca/~arnaud/doucet_godsill_andrieu_sequentialmontecarloforbayesfiltering.pdf

Compressive sampling and frontiers in Signal Processing: http://www.ima.umn.edu/2006-2007/ND6.4-15.07/abstracts.html

Clustering By Mixture Models (EM Algorithm for Mixture of Gaussian)     (Courtesy: http://www.stat.psu.edu/~jiali/)

Data Mining: http://www.stat.psu.edu/~jiali/course/stat597e/material.html

The EM Algorithm: http://www.seanborman.com/publications/EM_algorithm.pdf                                  http://www.ipam.ucla.edu/publications/inv2003/inv2003_3986.pdf