Resources

Note: Colorado State University in no way endorses, supports, or is affiliated with the resources listed on this page. The opinions expressed on any of the pages linked to from this page are entirely that of the person expressing them. We are in no way responsible for any information you receive from these sites and don't guarantee the correctness of answers you may receive on message boards, should you choose to use them.

Search the Net

Can't say this enough. When confused, go to www.google.com and search for the answers. If you are looking for a solution to an R question, try including keywords

         R splus s

in your search terms.

R

Introduction
- R project main site,
- An Introduction to R---Notes on R: A Programming Environment for Data Analysis and Graphics, by Venables and Smith
- Using R, from the University of Alaska, Fairbanks.
- Kickstarting R, some html pages that get you started using R. These notes refer to these R source files.
- Lecture slides by Russell Steele: lectures 2, 3, 4
- R Graphics, by Paul Murrell
- R graphics gallery
- Three-D Visualisation and Animation using the RGL package, by Ross Ihaka.
- Lecture notes on R, by Mark Handcock at U. of Washington. Try Lectures 3 through 11.
- RGL: 3D Real-Time Visualization Device System for R and the manual at the CRAN site for rgl
- simpleR: Using R for Introductory Statistics, by John Verzani
- Tip Sheet for R, by Paul Johnson.
- R Reference Card, by Jim Robison-Cox at Montana State U., 2pages.
- R Reference Card, by Tim Short, 4 pages.
- R for Octave Users, and for Matlab users, by Robin Hankin
Using R with Emacs
- EmacsStatisticalSystem, for running R inside emacs. See ESS Manual, Section 7, for help with installing ESS and Section 40, for help with using ESS,
- GNU Emacs
Other R Environments
- StatET, an Eclipse plug-in for R,
- Tinn-R, a free editor for MS Windows that allows you to run part or all of your code ina file in R directly. Thanks to Chuck Panaccione for pointing this out.
Examples
- Data Mining with R, by Luis Torgo,
- R examples from Applied Survival Analysis, by Hosmer and Lemeshow.
- R examples, by Jim Lindsey
More Depth
- For detailed information on R see the R Manuals.
- R News, the newsletter for R, three issues per year.
Benchmarks
- Speed comparison of various number crunching packages, including Matlab, R, and others.
RGtk
- Widgets in R, two examples by Artem Sokolov,
- The RGtk package,
- Putting RGtk to Work, by Jim Robinson-Cox,

LaTeX

Tutorials and guides
- LaTeX: from beginner to TeXPert,
- Getting to grips with Latex, tutorials by A. Roberts,
- LaTeX for Word Processor Users,
- Report Writing with LaTeX, from the University of Cambridge,
- Resources for students at the University of Leeds, including help with R and LaTeX.
- A Simplified Introduction to LaTeX, by H.J. Greenberg, 136 pages,
- The not so Short Introduction to LaTeX2e, by T. Oetiker, 145 pages
- How-to make a PDF-document from a LaTeX - source, by Patrick Jockel, including how to generate bookmarks automatically for table of contents entries,
- Latex Reference Card
Implementations
- The teTeX Homepage,
- LyX, a nice WYSIWYM LaTeX editor,
- MiKTeX, a TeX implementation for the Windows operating system,
- TeXnicCenter, an IDE for developing Latex documents on Microsoft Windows.
- TeXlipse, a plug-in for Eclipse for formatting in LaTeX,

Machine Learning

Machine Learning Repository at UCI. Includes links to data sets that are often used by machine learning researchers.
Machine Learning Mailing List on Google, recently created,
Machine Learning Mailing List, visit this site to read about how to subscribe to this periodic mailing, which includes announcements of jobs, meetings, publications, and other things.
Journal of Machine Learning Research, all papers are on-line.

Lasso

The Lasso Page, maintained by R. Tibshirani, one of our text book's authors.

Matrix Factorizations

Singular Value Decomposition (SVD) tutorial,

Neural Networks

Introduction to neural networks,

Support Vector Machines

Expectation Maximization

Expectation Maximization Algorithm, by S. Akaho and Olivier Michel, includes Java Applet demonstrating EM applied to multiple Gaussian model.
An Introduction to Statistical Machine Learning--EM for GMMs, slides by Samy Bengio.
The Expectation Maximization Algorithm, by Frank Dellaert
An Expectation-Maximization Approach to Nonlinear Component Analysis, by Rosipal and Girolami, Neural Computation, vol. 13, 505-510 (2001)
Use of R as a Toolbox for Mathematical Statistics Exploration, by Horton, Brown and Qian, The American Statistician, November 2004, Vol. 58, No. 4.

Reinforcement Learning

A Java applet, for training a robot to crawl, by Hajime Kimura.
Another applet showing Q learning at work on the crawling robot, like the previous link. This applet allows you to vary epsilon, gamma, and alpha as it runs.

Genetic Algorithms

Genitor Project at CSU, with publications including a genetic algorithm tutorial by Darrell Whitley.

Recent Applications

Metagenes and molecular pattern discovery using matrix factorization, Brunet, Tamayo, Golub, Mesirov, PNAS 2004, vol. 101, no. 12, pp. 4164-4169.

CS545 Machine Learning Spring 2008 Department of Computer Science

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