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International Conference on Multimodal Interfaces
Pascal

Tutorial on Statistical Machine Learning with Applications to Multimodal Processing

author: Samy Bengio, Google
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Slides
0:00 Tutorial on Statistical Machine Learning
0:01 Outline of the Tutorial
2:12 Part I
2:15 What is Machine Learning?
2:19 What is Machine Learning? (Graphical View)
2:38 What is Machine Learning?
3:45 Why Learning is Difficult?
3:50 Why Learning is Difficult?
5:02 Why Learning is Difficult? (2)
5:45 Why Learning is Difficult? (3)
6:23 Occam’s Razor’s Principle
7:58 Learning as a Search Problem
9:43 Types of Problems
9:47 Types of Problems1
10:24 Types of Problems2
10:47 Types of Problems3
11:25 Applications
11:28 Applications
13:30 Part II
14:19 Data, Functions, Risk
14:42 The Data
17:29 The Function Space
19:34 The Loss Function
21:22 The Risk and the Empirical Risk
24:10 The Risk and the Empirical Risk
26:22 The Training Error
28:03 The Capacity
28:06 The Capacity
32:10 Theoretical Curves
35:26 Theoretical Curves
37:41 Methodology
37:43 Methodology
39:59 Model Selection - Validation
43:05 Model Selection - Cross-validation
46:38 Estimation of the Risk - Validation
47:46 Estimation of the Risk - Cross-validation
47:49 Estimation of the Risk and Model Selection
50:02 Train - Validation - Test
50:05 Cross-validation + Test
50:07 Models
50:08 Examples of Known Models
52:31 Part III
53:01 Preliminaries
53:03 Reminder: Basics on Probabilities
54:28 Gaussian Mixture Models
54:30 What is a Gaussian Mixture Model
56:15 Characteristics of a GMM
57:36 Expectation-Maximization
57:41 Basics of Expectation-Maximization
60:23 EM for GMM (Graphical View, 1)
62:16 EM for GMM (Graphical View, 2)
62:50 EM for GMM (Graphical View, 3)
63:19 EM: More Formally
65:40 EM for GMMs
65:45 EM for GMM: Hidden Variable
67:30 EM for GMM: Auxiliary Function
69:17 EM for GMM: Auxiliary Function
71:01 EM for GMM: Update Rules
72:46 Initialization

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