Outline
Logistics
Course Topics by Week
Learning: Mature Technology
Defining a Learning Problem
Choosing the Training Experience
Choosing the Target Function
The Ideal Evaluation Function
Choosing Repr. of Target Function
Example: Checkers
Target Function
Representation
AI = Representation + Search
Concept Learning
Decision Tree Representation of Edible
Space of Decision Trees
Example: “Good day for tennis”
Experience: “Good day for tennis”
Decision Tree Representation
DT Learning as Search
Simplest Tree
Successors
To be decided:
Intuition: Information Gain
Entropy (disorder) is bad Homogeneity is good
Entropy
Information Gain
Gain of Splitting on Wind
Evaluating Attributes
Resulting Tree ….
Recurse!
One Step Later…
Overfitting…
Summary: Learning = Search
Hill Climbing is Incomplete
Version Spaces
Restricted Hypothesis Representation
Consistency
General to Specific Ordering
Correspondence A hypothesis = set of instances
Version Space: Compact Representation
Boundary Sets
Candidate Elimination Algorithm
Initialization
Training Example 1
Training Example 2
Training Example 3
A Biased Hypothesis Space
Comparison
An Unbiased Learner
Two kinds of bias
Formal model of learning
PAC Learning
Example of a PAC learner
Sample complexity
Infinite Hypothesis Spaces
Vapnik-Chervonenkis Dimension
Dichotomies of size 0 and 1
Dichotomies of size 2
Dichotomies of size 3 and 4
Ensembles of Classifiers
How voting helps
Constructing Ensembles
Review: Learning
Email: weld@cs.washington.edu
Other information: CSE 592, Lecture 8
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