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source: Nando de Freitas 2013年1月25日
Slides available at: http://www.cs.ubc.ca/~nando/540-2013/...
Course taught in 2013 at UBC by Nando de Freitas
introduction 59:58
linear prediction 1:04:20
Maximum likelihood and linear regression 1:14:01
Regularization and regression 1:01:16
regularization, cross-validation and data size 58:47
Bayesian learning 1:17:40
Bayesian learning part 2 21:10
Introduction to Gaussian processes 1:18:55
Gaussian processes 1:17:28
Bayesian optimization and multi-armed bandits 1:20:30
Decision trees 1:06:06
Random forests 1:16:55
Random forests applications 51:42
Unconstrained optimization 1:16:19
Logistic regression 1:13:47
Logistic regression 1:13:47
Neural networks 1:04:24
Deep learning I 1:15:05
Deep learning II, the Google autoencoders and dropout 1:12:03
Importance sampling and MCMC I 1:16:18
Markov chain Monte Carlo (MCMC) II 39:17
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