About
Machine Learning is a multidisciplinary field which aims to understand and design algorithms that automatically extract useful information from data. Since real world data are typically noisy, ambiguous and occasionally erroneous, a central requirement of a learning system is that it must be able to handle uncertainty. Probability theory provides an ideal basis for representing and manipulating uncertain knowledge, so many successful algorithms in machine learning are based on probabilistic i.e. Bayesian inference. Bayesian inference provides a principled framework for machine learning, but exact inference is often intractable, so most algorithms rely on approximations such as variational methods or Markov chain Monte Carlo.
More > http://talks.cam.ac.uk/show/archive/9091
Videos

01:53:15
Group Theory and Machine Learning
Mar 3, 2008
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35568 views

50:28
Spectral Clustering
Mar 3, 2008
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36059 views

01:20:04
Machine Learning Applications / Challenges in Natural Language Parsing
Mar 29, 2009
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4683 views

01:42:40
Prequential Statistics
Mar 29, 2009
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4338 views