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Statistical learning theory
Published on Aug 27, 200713930 Views
- Learning Theory: Foundations and Goals \\ - Learning Bounds: Ingredients and Results \\ - Implications: What to conclude from bounds \\
Related categories
Chapter list
Introduction to learning theory00:00
Goal of this course03:47
Not covered04:46
Outline04:56
First of all, what is a theory?-part0104:58
First of all, what is a theory?-part0205:33
First of all, what is a theory?-part0305:43
First of all, what is a theory?-part0405:54
Some more definitions05:59
What is a good theory?-part0107:07
What is a good theory?-part0207:32
What is a good theory?-part0307:37
What is a good theory?-part0408:11
What is a good theory?-part0508:29
What is learning?08:34
Recursion-part0109:56
Recursion-part0210:18
Recursion-part0310:28
Recursion-part0410:49
Rephrasing-part0110:59
Rephrasing-part0211:02
Rephrasing-part0311:08
Rephrasing-part0411:13
Learning theory: Why?11:32
Inductive principles13:05
Example 1: Probability of sunrise tomorrow-part0114:18
Example 1: Probability of sunrise tomorrow-part0215:31
Example 1: Probability of sunrise tomorrow-part0316:00
Example 1: Probability of sunrise tomorrow-part0416:15
Example 1: Probability of sunrise tomorrow-part0516:47
Example 2: extend a sequence of integers-part0118:13
Example 2: extend a sequence of integers-part0218:31
Example 2: extend a sequence of integers-part0318:52
Example 2: extend a sequence of integers-part0419:21
Example 2: extend a sequence of integers-part0519:48
Example 2: extend a sequence of integers-part0619:58
Example 2: extend a sequence of integers-part0720:38
Example 2: extend a sequence of integers-part0821:07
Example 3: sequence of integers (Hutter)-part0121:55
Example 3: sequence of integers (Hutter)-part0222:09
Example 3: sequence of integers (Hutter)-part0322:14
Example 3: sequence of integers (Hutter)-part0422:23
Example 4: sequence of digits (Hutter)-part0122:48
Example 4: sequence of digits (Hutter)-part0223:02
Example 4: sequence of digits (Hutter)-part0323:23
Example 4: sequence of digits (Hutter)-part0424:01
Example 4: sequence of digits (Hutter)-part0524:13
Inductive principle-part0124:57
Inductive principle-part0225:10
Inductive principle-part0325:28
Inductive principle-part0426:58
Probability: a nice tool for reasoning27:29
Probability interpretations-part0128:47
Probability interpretations-part0229:17
Probability interpretations-part0329:35
Probability as frequencies29:46
Proabilities as intrinsic properties30:46
Probabilities as degrees of belief31:38
Baye's rule33:12
Probabilities and proofs34:07
The need for assumptions-part0135:24
The need for assumptions-part0236:09
Settings-part0137:18
Settings-part0238:37
Settings-part0338:51
Settings-part0439:04
Settings-part0539:12
Settings vs. assumptions-part0139:35
Settings-part05A39:49
Settings vs. assumptions-part01A40:18
Settings vs. assumptions-part0240:32
Settings vs. assumptions-part0340:53
Settings vs. assumptions-part0441:36
Settings vs. assumptions-part0542:05
Settings and algorithms42:34
Data generation mechanisms43:58
Protocols45:20
Success measures46:12
Type of analysis46:51
Example: Bayesian inference47:46
Our goal50:52
Is this reasonable?51:53
Minimax estimation52:12
Notation-part0157:21
Notation-part0258:07