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
[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
