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Machine Learning and Data Mining: 02 Machine Learning

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Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture gives a very short introduction to the three main machine learning …

Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture gives a very short introduction to the three main machine learning paradigms.

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  • 1. Machine Learning Machine Learning and Data Mining
  • 2. Lecture outline 2 What is Machine Learning? What are the paradigm? Unsupervised Learning Supervised Learning Reinforcement Learning Prof. Pier Luca Lanzi
  • 3. What is Machine Learning? 3 “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” Tom Mitchell (1997) A program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. A well-defined learning task is defined by P, T, and E. Prof. Pier Luca Lanzi
  • 4. Example: checkers 4 Task T: playing checkers Artificial Intelligence Design and implement a computer-based system that exhibit intelligent action Machine Learning Write a program that can learn how to play It can learn from examples of previous games, by playing against another opponent, by playing against itself Prof. Pier Luca Lanzi
  • 5. Examples 5 A checker learning problem Task T: playing checkers Performance P: percent of games won against opponents Training experience E: playing practice games A handwriting recognition learning problem Task T: recognizing and classifying handwritten words withing images Performance P: percent of words correctly classified Training experience E: a database of handwritten words with given classification Prof. Pier Luca Lanzi
  • 6. Example 6 A robot driving learning problem Task T: driving on public four-lane highways using vision Performance P: average distance traveled before an error Training experience E: a sequence of images and steering commands recorded while observing a human driver Prof. Pier Luca Lanzi
  • 7. 7 Prof. Pier Luca Lanzi
  • 8. What is unsupervised learning? 8 Task T: finding interesting groups into data, learning “what normally happens” Performance P: how good, how interesting the groups are Training experience E: raw data Example applications Customer segmentation in CRM Color quantization for image compression, Bioinformatics Prof. Pier Luca Lanzi
  • 9. What is an apple? 9 Prof. Pier Luca Lanzi
  • 10. What is an apple? 10 Prof. Pier Luca Lanzi
  • 11. Are these apples? 11 Prof. Pier Luca Lanzi
  • 12. What is supervised learning? 12 Training experience E: examples labeled by a supervisor Task T: to extract a description of a concept from the data. Use the description to predict the output for future examples Performance P: how accurate the description is Example applications Credit approval Target marketing Medical diagnosis Fraud detection Prof. Pier Luca Lanzi
  • 13. What is Reinforcement Learning? 13 Prof. Pier Luca Lanzi
  • 14. What is Reinforcement Learning? 14 Agent stt+1 at rt+1 Environment The agent learn through trial-and-error interactions The goal is to maximize the amount of reward received from the environment Compute a value function Q(st,at) mapping state-action pairs into expected future payoffs Prof. Pier Luca Lanzi
  • 15. What is reinforcement learning? 15 Training experience E: online interactions with the environment Task T: collect as much reward as possible Performance P: the amount of reward Example applications Robot learning Games Multiagent learning Prof. Pier Luca Lanzi
  • 16. Algorithms, Paradigms, Applications 16 Applications Paradigms Agents Data Mining Unsupervised Learning Robotics Supervised Learning … Reinforcement Learning … Algorithms Clustering Association Rules Decision trees … Prof. Pier Luca Lanzi
  • 17. Machine Learning and Data Mining 17 Machine learning algorithms acquire structural descriptions from examples Structural descriptions represent patterns explicitly They can be used to predict outcome in new situations They can be used to understand and explain how prediction is derived Unsupervised learning Clustering Association rules Supervised learning Decision trees Decision rules Bayesian classifiers Prof. Pier Luca Lanzi