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Do you know any good lectures about the analysis of learning algorithms whose objectives are classification (learning theory of: active/semi-supervised/online ...) in terms complexity, convergence, error etc. I'm not familiar with this kind of analysis, so easy-to-understand lectures are more convenient. |
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At Carnegie Mellon they are offering an introductory machine learning course. Some of the lectures, in particular lectures 7-11, deal with aspects of learning theory. They seem pretty straightforward. |
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On videolectures.net you can find links to many previous machine learning summer school talks. Some of them have been about specific branches of learning theory, or introductory learning theory, etc. Here are some highlights:
A search for machine learning summer school shows many other talks. |
