Artificial Intelligence | Machine Learning
Instructor: Ng, Andrew
(return to course)
|Advice on applying machine learning:
Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.
A list of last year's final projects can be found here
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.
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.
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.
|Viewing PostScript and PDF files:
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Download complete set of course materials. (Includes all available handouts, assignments, exams, and computer software. Does not include video assets)
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