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CS 8520: Artificial Intelligence Machine Learning 1 Paula Matuszek Fall, 2005
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],[object Object],[object Object]
Learning Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learner Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Very Simple Learning Program ,[object Object],[object Object],[object Object],[object Object],[object Object]
Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Performer ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Critic ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learner ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
General Model of Learning Agent Environment Agent Critic Learning Element Problem Generator Performer with KB Performance Standard Sensors Effectors feedback learning goals changes knowledge
Some major paradigms of machine learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Approaches to Learning Systems ,[object Object],[object Object],[object Object],[object Object],[object Object]
Rote Learning: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interactive ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The inductive learning problem ,[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive Learning Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Model spaces ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rule Induction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rule Induction Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rose Diagnosis R1: If not yellow leaves and wilted leaves and brown spots then fungus. … R6: If wilted leaves and yellow leaves and not brown spots then bugs N Y Y Bugs Y N Y Fungus Y N N Fungus N N Y Nutrition Y Y N Bugs Y Y N Fungus Brown Spots Wilted Leaves Yellow Leaves
Rose Diagnosis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision Tree Induction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ID3 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Textbook restaurant domain ,[object Object],[object Object],[object Object],[object Object],[object Object]
A decision tree from introspection
A training set
ID3-induced decision tree
How well does it work? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluating Classifying Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary: Decision tree learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning by Analogy: Case-based Reasoning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Case-based Reasoning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Case-Based Retrieval ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Case-based Reasoning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Unsupervised Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
REALLY Unsupervised Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
More Aspects of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
More Aspects of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
More Aspects of Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Future ,[object Object],[object Object],[object Object],[object Object]
Some Current DARPA Programs and Solicitations ,[object Object],[object Object],[object Object]
The Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Finding Enough Data ,[object Object],[object Object],[object Object], 
Getting Critics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Online Uses for Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Machine Learning presentation.

  • 1. CS 8520: Artificial Intelligence Machine Learning 1 Paula Matuszek Fall, 2005
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  • 11. General Model of Learning Agent Environment Agent Critic Learning Element Problem Generator Performer with KB Performance Standard Sensors Effectors feedback learning goals changes knowledge
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  • 17.
  • 18.
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  • 21. Rose Diagnosis R1: If not yellow leaves and wilted leaves and brown spots then fungus. … R6: If wilted leaves and yellow leaves and not brown spots then bugs N Y Y Bugs Y N Y Fungus Y N N Fungus N N Y Nutrition Y Y N Bugs Y Y N Fungus Brown Spots Wilted Leaves Yellow Leaves
  • 22.
  • 23.
  • 24.
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  • 26. A decision tree from introspection
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