Advanced Machine Learning

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			Intro 4771 Tutorial Many background topics and additional information for 6772 are available at the 4771 web page. Please consult that at the following link for other papers, notes, tutorials, etc. to help along with 6772: 4771 Home Page Matlab Tutorial Matlab is one of the best tools for designing machine learning algorithms and many of the class assignments and class projects will be easiest to implement and explore with it. Alternatively, it is possible to use other mathematical software like Mathematica or MathCad although these will be much more awkward. Furthermore, it is possible to use C/C++ or Java as the implementation platform but you will require matrix libraries. Matlab is available to the Columbia community through AcIS. You just connect (i.e. using ssh) to an AcIS CUNIX machine like: ssh -lyourusername cunix.cc.columbia.edu And then run 'matlab' (which lives in /opt/local/bin/matlab). See the following for more details (Windows or Unix): Matlab Software through AcIS The Columbia University Computer Science department also has Matlab available on various Unix machines (in /usr/local/bin/matlab). Matlab Tutorials: Mathworks Matlab documentation MathWorks Matlab Machine Learning Tutorials UNH Matlab Tutorial US Navy Matlab Tutorial MTU Introduction to Matlab Example code (fits polynomial regression to x,y data): polyreg.m Example code (generates gaussian samples from a Gaussian mean and covariance matrix): genData.m Example code (plots a 2D Gaussian ellipse contour): plotGauss.m Example code (plots several Gaussians using the above function): plotClust.m Example code (randomly initializes and plots M Gaussians): randInit.m Example code (animates the motion capture data): animate.m