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Machine Learning, Data Mining, Genetic Algorithms, Neural Networks ISYS370 Dr. R. Weber
Concept Learning is a Form of Inductive Learning ,[object Object],[object Object],[object Object]
Concept Learning ,[object Object],[object Object]
Validation of Concept Learning i ,[object Object],[object Object],[object Object]
Validation of Concept Learning ii ,[object Object],[object Object],[object Object]
Rule Learning ,[object Object],[object Object],[object Object]
Decision trees ,[object Object],[object Object],[object Object],[object Object],[object Object]
Decision trees - leaf nodes (classes) - decision nodes (tests on attribute values) - from decision nodes branches grow for each possible outcome of the test From Cawsey, 1997
Decision tree induction ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],ID3 algorithm
[object Object],[object Object],[object Object],How does ID3 chooses tests
[object Object],[object Object],[object Object],Choosing tests
Bad 400 salaried 1,500 4 Very good 300 Waged 3,000 3 Very bad 600 Salaried 4,000 2 Good 200 Salaried 2,000 1 Loan status Repayment Job status Monthy income
[object Object],[object Object],Data mining tasks ii ,[object Object],[object Object],[object Object],[object Object]
KDD applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Text mining ,[object Object]
Text mining applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],deductive reasoning analogical reasoning inductive reasoning search Problem solving method Reasoning type
Genetic Algorithms (GA)
Genetic algorithms (i) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Genetic algorithms (ii) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genetic Algorithms ii ,[object Object],[object Object]
Neural Networks (NN)
[object Object],[object Object],the evidence
the evidence ,[object Object],[object Object]
the evidence ,[object Object],[object Object]
the evidence ,[object Object],[object Object]
NN: model of brains ,[object Object],neurons synapses electric transmissions :
Elements ,[object Object],[object Object],[object Object],[object Object]
terminology ,[object Object],[object Object],[object Object],[object Object],[object Object]
The concept => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 1  Yes, 0  No => mammal 1 1 0
The concept => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 1  Yes, 0  No
The concept => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0.5 0.5 0.5 1  Yes, 0  No
=> mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0*0.5+1*0.5+1*0.5= 1 1*0.5+0*0.5+0*0.5= 0.5 1*0.5+1*0.5+0*0.5= 1 Goal is to have weights that recognize different representations of mammals and birds as such 0.5 0.5 0.5
=> mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0*0.5+1*0.5+1*0.5= 1 1*0.5+0*0.5+0*0.5= 0.5 1*0.5+1*0.5+0*0.5= 1 Suppose we want bird to be greater 0.5 and mammal to be equal or less than 0.5 0.5 0.5 0.5
=> mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0*0.25+1*0.25+1*0.5= 0.75 1*0.25+0*0.25+0*0.5= 0.25 1*0.25+1*0.25+0*0.5= 0.5 Suppose we want bird to be greater 0.5 and mammal to be equal or less than 0.5 0.25 0.25 0.5
The training ,[object Object],[object Object],=> mammal (1) => bird (0) 0 1 1 4 legs flies eggs i=1 i=2 i=3 j=1 j=2 j=3 ij Goal minimize error between representation of the expected and actual outcome 20 ij 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1
[object Object]
Characteristics ,[object Object],[object Object],[object Object],[object Object]
Where are NN applicable? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Applications (i) ,[object Object],[object Object],[object Object]
Applications (ii) ,[object Object],[object Object],[object Object],[object Object]
CMU Driving ALVINN ,[object Object],[object Object],[object Object],[object Object]
Why using NN for the driving task? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],the neural network
Resources ,[object Object],[object Object],[object Object]

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Machine Learning, Data Mining, Genetic Algorithms, Neural ...

  • 1. Machine Learning, Data Mining, Genetic Algorithms, Neural Networks ISYS370 Dr. R. Weber
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  • 8. Decision trees - leaf nodes (classes) - decision nodes (tests on attribute values) - from decision nodes branches grow for each possible outcome of the test From Cawsey, 1997
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  • 13. Bad 400 salaried 1,500 4 Very good 300 Waged 3,000 3 Very bad 600 Salaried 4,000 2 Good 200 Salaried 2,000 1 Loan status Repayment Job status Monthy income
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  • 31. The concept => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 1  Yes, 0  No => mammal 1 1 0
  • 32. The concept => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 1  Yes, 0  No
  • 33. The concept => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0.5 0.5 0.5 1  Yes, 0  No
  • 34. => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0*0.5+1*0.5+1*0.5= 1 1*0.5+0*0.5+0*0.5= 0.5 1*0.5+1*0.5+0*0.5= 1 Goal is to have weights that recognize different representations of mammals and birds as such 0.5 0.5 0.5
  • 35. => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0*0.5+1*0.5+1*0.5= 1 1*0.5+0*0.5+0*0.5= 0.5 1*0.5+1*0.5+0*0.5= 1 Suppose we want bird to be greater 0.5 and mammal to be equal or less than 0.5 0.5 0.5 0.5
  • 36. => mammal => bird 0 1 1 4 legs fly lay eggs 1 0 0 => mammal 1 1 0 0*0.25+1*0.25+1*0.5= 0.75 1*0.25+0*0.25+0*0.5= 0.25 1*0.25+1*0.25+0*0.5= 0.5 Suppose we want bird to be greater 0.5 and mammal to be equal or less than 0.5 0.25 0.25 0.5
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Hinweis der Redaktion

  1. What is predictive modeling? Predictive modeling uses demographic, medical and pharmacy claims information to determine the range and intensity of medical problems for a given population of insured persons. This assessment of risk allows health plans, payers and provider groups to plan, evaluate and fund health care management programs more effectively. From: http://www.dxcgrisksmart.com/faq.html
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