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Friday, 12 January 2007 William H. Hsu Department of Computing and Information Sciences, KSU http:// www.kddresearch.org http:// www.cis.ksu.edu/~bhsu Readings: Chapter 1, Mitchell A Brief Survey of Machine Learning CIS 732/830: Lecture 0
Lecture Outline ,[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],[object Object],[object Object]
Course Information and Administrivia ,[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],[object Object]
Class Resources ,[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],[object Object]
Course Overview ,[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]
Why Machine 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]
Rule and Decision Tree Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Neural Network Learning ,[object Object],[object Object],[object Object]
Relevant Disciplines ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Machine Learning Symbolic Representation Planning/Problem Solving Knowledge-Guided Learning Bayes’s Theorem Missing Data Estimators PAC Formalism Mistake Bounds Language Learning Learning to Reason Optimization Learning Predictors Meta-Learning Entropy Measures MDL Approaches Optimal Codes ANN Models Modular Learning Occam’s Razor Inductive Generalization Power Law of Practice Heuristic Learning Bias/Variance Formalism Confidence Intervals Hypothesis Testing
Specifying A Learning Problem ,[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],[object Object],[object Object]
Example: Learning to Play Checkers ,[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]
A Target Function for Learning to Play Checkers ,[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]
A Training Procedure for Learning to Play Checkers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Design Choices for Learning to Play Checkers Completed Design Determine Type of Training Experience Games against experts Games against self Table of correct moves Determine Target Function Board  value Board  move Determine Representation of Learned Function Polynomial Linear function of six features Artificial neural network Determine Learning Algorithm Gradient descent Linear programming
Some Issues in Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interesting Applications Reasoning (Inference, Decision Support) Cartia ThemeScapes - http:// www.cartia.com 6500 news stories from the WWW in 1997 Planning, Control Normal Ignited Engulfed Destroyed Extinguished Fire Alarm Flooding DC-ARM - http://www- kbs.ai.uiuc.edu Database Mining NCSA D2K - http:// alg.ncsa.uiuc.edu
Lecture Outline ,[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],[object Object]
What to Learn? ,[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],[object Object],[object Object]
How to Learn It? ,[object Object],[object Object],[object Object],[object Object],[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],[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]
A Learning Problem Unknown Function x 1 x 2 x 3 x 4 y = f (x 1 , x 2 , x 3 , x 4 ) ,[object Object],[object Object]
Hypothesis Space: Unrestricted Case ,[object Object],[object Object],[object Object],[object Object],[object Object]
Training Examples for Concept EnjoySport ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Representing Hypotheses ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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original

  • 1. Friday, 12 January 2007 William H. Hsu Department of Computing and Information Sciences, KSU http:// www.kddresearch.org http:// www.cis.ksu.edu/~bhsu Readings: Chapter 1, Mitchell A Brief Survey of Machine Learning CIS 732/830: Lecture 0
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  • 14. Design Choices for Learning to Play Checkers Completed Design Determine Type of Training Experience Games against experts Games against self Table of correct moves Determine Target Function Board  value Board  move Determine Representation of Learned Function Polynomial Linear function of six features Artificial neural network Determine Learning Algorithm Gradient descent Linear programming
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  • 16. Interesting Applications Reasoning (Inference, Decision Support) Cartia ThemeScapes - http:// www.cartia.com 6500 news stories from the WWW in 1997 Planning, Control Normal Ignited Engulfed Destroyed Extinguished Fire Alarm Flooding DC-ARM - http://www- kbs.ai.uiuc.edu Database Mining NCSA D2K - http:// alg.ncsa.uiuc.edu
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