CS545Machine Learning Spring 2008 Department of Computer Science |

R splus sin your search terms.

- 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
- 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,

- 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 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.

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

- Course at Stanford on Text Retrieval and Mining,
- Course at Brown on Machine Learning,
- Links to SVM info collected by S. Thruh
- Proximal Support Vector Machines

- 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.

- The Matrix Cookbook, by K. Petersen

- Communications of the ACM, Special Issue on Evolving Data Mining into Solutions for Insights, viewable from your CSU account

- 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.

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

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