Machine Learning (2012) by Nando de Freitas at UBC

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source: Nando de Freitas     2012年11月1日
The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/...
undergraduate machine learning at UBC 2012

1: Introduction to machine learning 46:17
2: Introduction to machine learning 2 50:48
3: Basic probability 53:54
4: Introduction to probability, linear algebra and pagerank 53:55
5: Introduction to Bayes 9:30
6: Bayes rule and Bayesian networks 51:44
8: Inference in Bayesian networks and dynamic programming 49:50
9: Hidden Markov models - HMM 52:23
10: Expectation, probability and Bernoulli models 42:21
11: Maximum likelihood 32:55
12: Bayesian learning 53:10
13: Learning Bayesian networks 53:28
14: Linear algebra revision for machine learning and web search 52:38
15: Singular Value Decomposition - SVD 51:56
16: Principal Component Analysis - PCA 51:32
18: Least squares and the multivariate Gaussian 42:59
7: Bayesian networks, aka probabilistic graphical models 45:00
17: Linear prediction 51:16
20: Cross-validation, big data and regularization 1:30:53
21: L1 regularization and the lasso 20:20
22: Sparse models and variable selection 1:11:40
22: Sparse models and variable selection 1:11:40
23: Dirichlet and categorical distributions 46:40
24: Text classification with Naive Bayes 43:49
25: Twitter sentiment prediction with Naive Bayes 48:39
26: Optimization 49:02
27: Logistic regression 51:13
28: Neural networks 26:20
29: Neural nets and backpropagation 40:14
30: Deep learning 50:05
31: Decision trees 39:42
32: Random forests 33:11
33: Random forests, face detection and Kinect 38:30