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Lecture7 Ml Machines That Can Learn

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Kodok Ngorex
Kodok Ngorex
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Machines That Can Learn Reading: Chapter 9 from Marakas Additionally: Chapter 2 from Leake “Case Based Reasoning” – in the library short loan
What is Machine Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Fuzzy Logic and Linguistic Ambiguity ,[object Object],[object Object],[object Object],[object Object]
The Basics of Fuzzy Logic ,[object Object],[object Object],[object Object]
A Simple Membership Function Example ,[object Object],[object Object],[object Object]
Fuzziness Versus Probability ,[object Object],[object Object],[object Object],[object Object],[object Object]
Advantages and Limitations of Fuzzy Logic ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Artificial Neural Networks ,[object Object],[object Object],[object Object],[object Object]
Learning From Experience ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Fundamentals of Neural Computing ,[object Object],[object Object],[object Object],[object Object]
A Neuron synapse Inputs Increasing pulse at synaptic connection results in learning or oppositely in forgetting
Putting a Brain in a Box ,[object Object],[object Object],[object Object],[object Object],Carry weights (synapses)
Inside the Neurode ,[object Object],[object Object],[object Object],[object Object],Output layer Weighted inputs State function Bias input (normally=1) -ve +ve -ve low moderate high Transfer function A Neurode
Training the Artificial Neural Network Present data to the net with known outputs and let it “guess”. If calculation is wrong, weights are adjusted. Each neurode is tested for sensitivity
ANN Learning Paradigms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],** For the mathematically minded the algorithm is quoted in the chapter appendix
Benefits Associated with Neural Computing ,[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]
Genetic Algorithms and Genetically Evolved Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction to Genetic Algorithms ,[object Object],[object Object],[object Object],[object Object]
Basic Components of a Genetic Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Basic Process Flow of a Genetic Algorithm
Decoding, Crossover, Mutation City coding Miami ….. 000 Atlanta …..001 Chicago …010 Distance coding Miami ….. 00000000 Atlanta …..10001111 Chicago …10101011 Time coding Miami ….. 010 Atlanta …..100 Chicago …100 010 10101011 100 11000101 11101011 10101011 10000101 Before crossover After crossover 11000101 11100101 Before mutation After mutation
Benefits and Limitations Associated With GAs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications of ANNs and GAs ,[object Object],[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],[object Object],[object Object],[object Object],[object Object],[object Object]
Application areas? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cases and Indexes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How does it work? Retrieve case from library Propose solution Adapt Justify Criticise Evaluate Store Modified from Leake New problem
The process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Advantages of CBR ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Key Point Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Lecture7 Ml Machines That Can Learn

  • 1. Machines That Can Learn Reading: Chapter 9 from Marakas Additionally: Chapter 2 from Leake “Case Based Reasoning” – in the library short loan
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11. A Neuron synapse Inputs Increasing pulse at synaptic connection results in learning or oppositely in forgetting
  • 12.
  • 13.
  • 14. Training the Artificial Neural Network Present data to the net with known outputs and let it “guess”. If calculation is wrong, weights are adjusted. Each neurode is tested for sensitivity
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. Basic Process Flow of a Genetic Algorithm
  • 21. Decoding, Crossover, Mutation City coding Miami ….. 000 Atlanta …..001 Chicago …010 Distance coding Miami ….. 00000000 Atlanta …..10001111 Chicago …10101011 Time coding Miami ….. 010 Atlanta …..100 Chicago …100 010 10101011 100 11000101 11101011 10101011 10000101 Before crossover After crossover 11000101 11100101 Before mutation After mutation
  • 22.
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  • 27. How does it work? Retrieve case from library Propose solution Adapt Justify Criticise Evaluate Store Modified from Leake New problem
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