Advice on applying machine learning:
Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.
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Previous projects:
A list of last year's final projects can be found here
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Matlab Resources
Here are a couple of Matlab tutorials that you might find helpful: Matlab Tutorial and A Practical Introduction to Matlab. For emacs users only: If you plan to run Matlab in emacs, here are matlab.el, and a helpful emac's file.
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Octave Resources
For a free alternative to Matlab, check out GNU Octave. The official documentation is available here. Some useful tutorials on Octave include Octave Tutorial and Octave on Wiki.
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Data
Here is the UCI Machine Learning Repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences Neural Information Processing Systems (all old NIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.
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Viewing PostScript and PDF files:
Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one.
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