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