Machine Learning Summer School (MLSS), Kioloa 2008

Foundations of Machine Learning

author: Marcus Hutter, IDSIA
published: March 11, 2008, recorded: March 2008, views: 1339

Slides

Slides
0:00	Foundations of Machine Learning
1:39	Overview
7:15	Abstract
7:44	Table of Contents
8:28	Philosophical Issues: Contents
8:33	Philosophical Issues: Abstract
8:34	Philosophical Problems
9:37	On the Foundations of Machine Learning
11:51	Example 1: Probability of Sunrise Tomorrow
16:18	Example 2: Digits of a Computable Number
17:21	Example 3: Number Sequences
21:36	Occam's Razor to the Rescue
26:37	Grue Emerald Paradox
30:01	Confirmation Paradox
32:58	Problem Setup
34:38	What This Tutorial is (Not) About
36:13	Sequential/Online Prediction - Setup
38:05	Bayesian Sequence Prediction: Contents
39:19	Uncertainty and Probability
40:34	Frequency Interpretation: Counting
42:55	Objective Interpretation: Uncertain Events
44:52	Subjective Interpretation: Degrees of Belief
47:11	Bayes' Famous Rule
50:40	Example: Bayes' and Laplace's Rule (1)
53:17	Example: Bayes' and Laplace's Rule (2)

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Description

Machine learning is usually taught as a bunch of methods that can solve a bunch of problems (see above).

The second part of the tutorial takes a step back and asks about the foundations of machine learning, in particular the (philosophical) problem of inductive inference, (Bayesian) statistics, and artificial intelligence.

It concentrates on principled, unified, and exact methods.