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Machine Learning System Modelling and Analysis Burak Tiftik

Understand the fundamentals of machine learning, from its historical roots to the various types of algorithms used and real-world applications. Dive into supervised and unsupervised learning techniques, classification, regression, and clustering. Explore examples of machine learning in action, such as self-driving cars and spam filters. Discover how machine learning impacts our daily lives and business world, paving the way for exciting advancements.

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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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