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Machine Learning System Modelling and Analysis Burak Tiftik

Machine Learning System Modelling and Analysis Burak Tiftik. Summary. What is Machine Learning History of Machine Learning Problem Types in Machine Learning Types of Algorithms Used Example of Machine Learning in Action Conclusion Questions. What is Machine Learning.

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Machine Learning System Modelling and Analysis Burak Tiftik

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  1. Machine Learning System Modelling and Analysis Burak Tiftik

  2. Summary • What is Machine Learning • History of Machine Learning • Problem Types in Machine Learning • Types of Algorithms Used • Example of Machine Learning in Action • Conclusion • Questions

  3. What is Machine Learning • Programs that can learn from data without explicit programming. Example: • Car driving itself • Program that estimates house prices • Spam filter

  4. Daily Examples • Mailing office automatic zipcode reader • Bank applications on understanding unathorized usage of cards • Online shopping suggestion methods • Fingerprint recognition • Face recognition • Handwriting recognition

  5. History of Machine Learning • Checkers 1952 Arthur Samuel, IBM for IBM 701 • Beats Connecticut champion 1962 • Who then beats the program 6 times in a row! • Alpha vs. Beta approach

  6. History of Machine Learning • 1957 Perceptron by Frank Rosenblatt in Cornell Aeronautical Lab • A type of neural network • 1969 Martin Minsky publishes a paper finding faults in perceptrons

  7. History of Machine Learning • 1967 Pattern Recognition • First algorithm is K-NN!

  8. History of Machine Learning • 1970's were not great for Machine Learning • Time of Expert Systems

  9. History of Machine Learning • 1980 Decision Tree model becomes available • Understandable by humans

  10. History of Machine Learning • 1980 Multi-Layered Neural Networks • Perceptrons upgraded • Can solve much complex problems

  11. History of Machine Learning • 1995 State Vector Machine (SVM) Vladimir N. Vapnik and Corrina Cortes • Used in text recognition, classification of messages • Classification and regression

  12. Problems, Algorithms Types, Algorithms • There is a disctinction between terminology • Problem? • Algorithm Type? • Algorithm?

  13. Types of Problems • Classification • Regression • Clustering • And many more

  14. Classification • Distinguishing between different things • Provided we know what these things are • Example: Character recognition • Methods: Support Vector Machines, Neural Networks, Naive Bayes classifier, Decision Trees, K-NN

  15. Regression • Predicting future input results from previous results • Example: House prices • Methods: Linear Regression, Non-Linear Regression, Generalized Linear Models, Decision Trees, Neural Networks

  16. Clustering • Given random data, grouping things by similarity • Example: Sort books in library by content • Methods: Hierarchical Clustering, k-means Clustering, Gaussian Mixture Models, Self-Organizing Maps

  17. Types of Algorithms • Supervised Learning • Unsupervised Learning • Transduction • Reinforcement Learning • And many more

  18. Supervised Learning • A training data is available • Training data contains input and desired output • Once trained, it is ready to use • Used in Classification and Regression • Methods: Decision Trees, K-NN, Linear Regression, Naive Bayes classifier, Neural Networks, Support Vector Machines, Case Based Reasoning ...

  19. Unsupervised Learning • Training data is not available or not used • Focus is on seperation of raw data into parts • Used in Clustering • Methods: K-means, hidden Markov models, Self-Organizing Maps, Hierarchical Clustering ...

  20. Example • Learning to win: case based plan selection in real-time strategy game • By David W. Aha, Matthew Molineaux and Marc Ponsen • Implemented in Wargus, clone of Warcraft II • Uses Case Based Reasoning, which is a method in supervised learning • Beats all available script based AI

  21. Wargus • Real Time Strategy game • Open Source • 2 sides fight another • Buildings, units

  22. How It Works • Buildings are used to calculate possible production • Ability to produce different units dictate AI move • Learns in time

  23. Quick Refresher Case: Current Case Computer Heat: 130 Computer Speed: 55 Does computer turn on?: Yes Does sound work?: Yes Hard Disk Remaining: 6 gb Case: Log 11 Computer Heat: 120 Computer Speed: 60 Does computer turn on?: Yes Does sound work?: Yes Hard Disk Remaining: 40 gb • Red Text implies distance between two cases. • Black text is weighted more than any other as problem itself is speed. • Sound is weighted little for instance as it is unrelated

  24. How It Works • Variables in CBR Algorithm

  25. Euclidean Distance Refresher Calculates similarity between points using distance

  26. Best Case Selector • C is past case • S is new case • Priority is on distance and secondarily on performance (similarity)

  27. What Do These Mean?

  28. What Do These Mean 2?

  29. Conclusion • Machine Learning is a huge field that is concerned with learning from data • It has huge impact on our lives even without our knowing • Has a lot of usage in business world where large amounts of data is common place • Exciting field!

  30. Questions?

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