Recorded: September 2009
at the Department of Electrical Engineering and Computer Sciences, UC Berkeley.
Part 1 of 2.
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0:00 Are You a Bayesian or a Frequentist?
1:28 Statistical Inference
6:37 Machine Learning
10:11 Decison-Theoretic Perspective -1
13:01 Decison-Theoretic Perspective -2
18:19 Decison-Theoretic Perspective -3
19:55 Coherence and Calibration
25:23 The Bayesian World
27:04 Subjective Bayes
34:03 Objective Bayes
36:44 Frequentist Perspective
39:21 Frequentist Activities
50:35 Outline -1
50:50 Surrogate Loss Functions
52:02 Motivating example: Decentralized Detection
53:18 Decentralized Detection
54:27 Decentralized Detection (cont.)
56:57 Perspectives -1
1:00:16 f-divergences
1:01:24 Why the f-divergence?
1:04:03 Statistical Machine Learning Perspective
1:04:48 Margin-Based Loss Functions
1:05:40 Estimation Based on a Convex Surrogate Loss
1:07:02 Some Theory for Surrogate Loss Functions
1:08:38 Outline -2
1:09:39 Setup -1
1:11:28 Profiling
1:13:44 Some Examples
1:14:19 Link -1
1:15:00 Conjugate Duality
1:15:49 Link -2
1:17:12 The Easy Direction
1:18:20 The Direction Has a Constructive Consequence -1
1:19:07 Example - Hellinger distance
1:20:45 Example - Variational distance
1:21:24 Example - Kullback-Leibler divergence
1:21:29 Bayes Consistency -1
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Source: videolectures.net/mlss09uk_jor...