Summary Learning from examples

Outline

[read Chapter 2]
[suggested exercises 2.2, 2.3, 2.4, 2.6]
 Learning from examples
 General-to-speci c ordering over hypotheses
 Version spaces and candidate elimination
algorithm
 Picking new examples
 The need for inductive bias

Note: simple approach assuming no noise,
illustrates key concepts




22 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997

, Training Examples for EnjoySport

Sky Temp Humid Wind Water Forecst EnjoySpt
Sunny Warm Normal Strong Warm Same Yes
Sunny Warm High Strong Warm Same Yes
Rainy Cold High Strong Warm Change No
Sunny Warm High Strong Cool Change Yes
What is the general concept?




23 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997

, Representing Hypotheses

Many possible representations
Here, h is conjunction of constraints on attributes
Each constraint can be
 a spec c value (e.g., Water = Warm)
 don't care (e.g., \Water =?")
 no value allowed (e.g.,\Water=;")
For example,
Sky AirTemp Humid Wind Water Forecst
hSunny ? ? Strong ? Samei




24 lecture slides for textbook Machine Learning, T. Mitchell, McGraw Hill, 1997