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Learning Objectives At the end of the class you should be able to: Explain the components and the architecture of a learning problem Explain why a learner needs a bias Identify the sources of error for a prediction D. Poole and A. Mackworth

  1. Learning Objectives At the end of the class you should be able to: Explain the components and the architecture of a learning problem Explain why a learner needs a bias Identify the sources of error for a prediction � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 1

  2. Learning Learning is the ability to improve one’s behavior based on experience. The range of behaviors is expanded: the agent can do more. The accuracy on tasks is improved: the agent can do things better. The speed is improved: the agent can do things faster. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 2

  3. Components of a learning problem The following components are part of any learning problem: task The behavior or task that’s being improved. For example: classification, acting in an environment data The experiences that are being used to improve performance in the task. measure of improvement How can the improvement be measured? For example: increasing accuracy in prediction, new skills that were not present initially, improved speed. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 3

  4. Black-box Learner Experiences/ Problem/ Data Task Model(s) Learner Reasoner Background knowledge/ Answer/ Bias Performance � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 4

  5. Learning architecture Experiences/ Problem/ Data Task Model(s) Learner Reasoner Background knowledge/ Answer/ Bias Performance � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 5

  6. Common Learning Tasks Supervised classification Given a set of pre-classified training examples, classify a new instance. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 6

  7. Common Learning Tasks Supervised classification Given a set of pre-classified training examples, classify a new instance. Unsupervised learning Find natural classes for examples. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 7

  8. Common Learning Tasks Supervised classification Given a set of pre-classified training examples, classify a new instance. Unsupervised learning Find natural classes for examples. Reinforcement learning Determine what to do based on rewards and punishments. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 8

  9. Common Learning Tasks Supervised classification Given a set of pre-classified training examples, classify a new instance. Unsupervised learning Find natural classes for examples. Reinforcement learning Determine what to do based on rewards and punishments. Analytic learning Reason faster using experience. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 9

  10. Common Learning Tasks Supervised classification Given a set of pre-classified training examples, classify a new instance. Unsupervised learning Find natural classes for examples. Reinforcement learning Determine what to do based on rewards and punishments. Analytic learning Reason faster using experience. Inductive logic programming Build richer models in terms of logic programs. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 10

  11. Common Learning Tasks Supervised classification Given a set of pre-classified training examples, classify a new instance. Unsupervised learning Find natural classes for examples. Reinforcement learning Determine what to do based on rewards and punishments. Analytic learning Reason faster using experience. Inductive logic programming Build richer models in terms of logic programs. Statistical relational learning learning relational representations that also deal with uncertainty. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 11

  12. Example Classification Data Training Examples: Action Author Thread Length Where e1 skips known new long home e2 reads unknown new short work e3 skips unknown old long work e4 skips known old long home e5 reads known new short home e6 skips known old long work New Examples: e7 ??? known new short work e8 ??? unknown new short work We want to classify new examples on feature Action based on the examples’ Author , Thread , Length , and Where . � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 12

  13. Feedback Learning tasks can be characterized by the feedback given to the learner. Supervised learning What has to be learned is specified for each example. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 13

  14. Feedback Learning tasks can be characterized by the feedback given to the learner. Supervised learning What has to be learned is specified for each example. Unsupervised learning No classifications are given; the learner has to discover categories and regularities in the data. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 14

  15. Feedback Learning tasks can be characterized by the feedback given to the learner. Supervised learning What has to be learned is specified for each example. Unsupervised learning No classifications are given; the learner has to discover categories and regularities in the data. Reinforcement learning Feedback occurs after a sequence of actions. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 15

  16. Measuring Success The measure of success is not how well the agent performs on the training examples, but how well the agent performs for new examples. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 16

  17. Measuring Success The measure of success is not how well the agent performs on the training examples, but how well the agent performs for new examples. Consider two agents: ◮ P claims the negative examples seen are the only negative examples. Every other instance is positive. ◮ N claims the positive examples seen are the only positive examples. Every other instance is negative. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 17

  18. Measuring Success The measure of success is not how well the agent performs on the training examples, but how well the agent performs for new examples. Consider two agents: ◮ P claims the negative examples seen are the only negative examples. Every other instance is positive. ◮ N claims the positive examples seen are the only positive examples. Every other instance is negative. Both agents correctly classify every training example, but disagree on every other example. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 18

  19. Bias The tendency to prefer one hypothesis over another is called a bias. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 19

  20. Bias The tendency to prefer one hypothesis over another is called a bias. Saying a hypothesis is better than N ’s or P ’s hypothesis isn’t something that’s obtained from the data. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 20

  21. Bias The tendency to prefer one hypothesis over another is called a bias. Saying a hypothesis is better than N ’s or P ’s hypothesis isn’t something that’s obtained from the data. To have any inductive process make predictions on unseen data, an agent needs a bias. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 21

  22. Bias The tendency to prefer one hypothesis over another is called a bias. Saying a hypothesis is better than N ’s or P ’s hypothesis isn’t something that’s obtained from the data. To have any inductive process make predictions on unseen data, an agent needs a bias. What constitutes a good bias is an empirical question about which biases work best in practice. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 22

  23. Learning as search Given a representation, data, and a bias, the problem of learning can be reduced to one of search. Learning is search through the space of possible representations looking for the representation or representations that best fits the data, given the bias. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 23

  24. Learning as search Given a representation, data, and a bias, the problem of learning can be reduced to one of search. Learning is search through the space of possible representations looking for the representation or representations that best fits the data, given the bias. These search spaces are typically prohibitively large for systematic search. E.g., use gradient descent or stochastic simulation. A learning algorithm is made of a search space, an evaluation function, and a search method. � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 24

  25. Data Data isn’t perfect: ◮ the features given are inadequate to predict the classification ◮ there are examples with missing features ◮ some of the features are assigned the wrong value � D. Poole and A. Mackworth 2016 c Artificial Intelligence, Lecture 7.1, Page 25

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