Machine Learning: Computational Learning Theory and Batch Learning - Lecture 12

Machine Learning: Computational Learning Theory and Batch Learning - Lecture 12

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Two Directions for How good is our learning algorithm?

4 of 12

4 of 12

Two Directions for How good is our learning algorithm?

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Machine Learning: Computational Learning Theory and Batch Learning - Lecture 12

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  1. 1 Intro
  2. 2 Checkpoint: The bigger picture
  3. 3 Learning Conjunctions Some random source (nature) provides training examples Teacher (Nature) provides the labels (f(x))
  4. 4 Two Directions for How good is our learning algorithm?
  5. 5 The mistake bound approach
  6. 6 The setup
  7. 7 Distribution over the instance space
  8. 8 PAC Learning - Intuition
  9. 9 Error of a hypothesis
  10. 10 Empirical error
  11. 11 The goal of batch learning
  12. 12 Online learning vs. Batch learning

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