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Machine Learning Sections 18.1 - 18.4
What is learning? ,[object Object],[object Object],[object Object]
Why learn? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Components of a learning system
Evaluating Performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Major Paradigms of ML ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Major Paradigms (Cont). ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised Concept Learning ,[object Object],[object Object],[object Object]
Inductive Bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive learning framework ,[object Object],[object Object],[object Object],[object Object],[object Object]
Case-based idea ,[object Object],[object Object],[object Object],[object Object]
Nearest Neighbor ,[object Object],[object Object],[object Object],[object Object],[object Object]
k-nearest neighbor ,[object Object],[object Object],[object Object]
Nearest-neighbor problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nearest-neighbor results
Learning Decision Trees ,[object Object],[object Object],[object Object],[object Object]
Decision Tree Example
Building Decision Trees ,[object Object],[object Object],[object Object]
Construction Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How to pick the “best” attribute? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Max Gain background ,[object Object],[object Object],[object Object],[object Object]
Expected questions remaining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Content ,[object Object],[object Object],[object Object],[object Object],[object Object]
Perfect Balance
Example: Homogeneity ,[object Object],[object Object],[object Object]
Low Information Content ,[object Object],[object Object],[object Object]
Information Gained ,[object Object],[object Object],[object Object],[object Object]
MaxGain definitions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information remaining ,[object Object],[object Object],i=1 n
Information Gain ,[object Object],[object Object]
Select the best attribute ,[object Object],[object Object],[object Object]
Example Data
Remainder(Color) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Gain Result
Final Decision Tree R B G Big Small
Extensions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Pruning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Generation of rules ,[object Object],[object Object],[object Object]
Setting Parameters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cross Validation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WillWait from 12 Examples
Increasing Training Set
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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