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MCMC Learning
Published on Aug 20, 20152053 Views
The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas. This theo
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Chapter list
MCMC Learning00:00
PAC Learning00:06
Agnostic Learning01:02
Challenge01:59
Uniform Distribution Learning02:26
But ...04:12
Markov Random Fields - 104:44
Markov Random Fields - 205:36
Learning Model (PAC/Agnostic)06:28
Gibbs Sampling (MCMC Algorithm)07:09
Ising Model07:41
Harmonic Analysis Using Eigenvectors - 108:40
Harmonic Analysis Using Eigenvectors - 209:43
Power Iteration to Find Eigenvectors - 110:13
Power Iteration to Find Eigenvectors - 211:25
When can we do this?12:18
Discrete Spectrum13:24
Learning Algorithm13:59
Main Results15:26
Open Questions17:00