R Reference Materials

This post contains pointers to R reference materials, and will be updated.

• Google search for R Tutorial
• An Introduction to R (pdf), from the central R web site, something between a tutorial and a manual
• Jo Hardin's R Tutorial (pdf), shorter
• Notes from Thomas Lumley's two-day short course in R
• Video R Tutorial
• Short html R Tutorial with implausibly poor colour choice
• Short well-organised html R Tutorial with that National Geographic look
• Yet another html R Tutorial, one big page with nice graphics and examples

Lecture Notes

More lecture notes, thanks to Paul Murray (!)
notes3 (w/ annotations)

Support Vector Machines

Wikipedia has a reasonably good entry on Support Vector Machines. The original paper proposing the technique is also a good resource, quite readable with good motivation: Corinna Cortes and Vladimir N. Vapnik (1995, Support-Vector Networks, Machine Learning 20).

Lecture Notes

Here are some lecture notes, thanks to Paul Murray.
notes1 (w/ annotations)
notes2 (w/ annotations)

Machine Learning Textbooks: Excellent and Online

This is a list of textbooks about machine learning which are (a) really good, and (b) free on the web.

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (deep coverage of an important subset of the field)
• Information Theory, Inference, and Learning Algorithms, by David MacKay (very mathematical approach, excellent but hard core)
• Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto (dated, but the bible of reinforcement learning)
• Bayesian Reasoning and Machine Learning, by David Barber (incomplete coverage of the field, but solid and accessible and includes Octave/Matlab code)
• Machine Learning by Simon Rogers (I will be using some of the chapters as lecture notes)
• Introduction to Machine Learning: Draft of Incomplete Notes by Nils J. Nilsson (excellent writer; his earlier book on AI saved my bacon; slightly dated)

If you know of others (criteria: of general Machine Learning interest, not something highly specific like Gaussian Processes) post a comment and I'll add above.

(Updated 13-Oct-2010)

Digits Dataset

The MNIST dataset of labelled handwritten digits: http://yann.lecun.com/exdb/mnist/.  (Thanks to Yann LeCun and Corrina Cortes for cleaning the dataset and making it publicly available.)

R

Install and play with (i.e., learn) R: http://www.r-project.org