This document provides an introduction to machine learning concepts including:
- Machine learning involves learning parameters of probabilistic models from data.
- Maximum likelihood and maximum a posteriori estimation are common techniques for learning parameters.
- Inductive learning involves constructing a hypothesis from examples to generalize the target function to new examples. Cross-validation is used to evaluate hypotheses on held-out data and avoid overfitting.
27. Inductive Learning Scheme + + + + + + + + + + + + - - - - - - - - - - - - Example set X {[A, B, …, CONCEPT]} Hypothesis space H {[CONCEPT(x) S(A,B, …)]} Training set D Inductive hypothesis h
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29. Multiple Inductive Hypotheses h 1 NUM(r) BLACK(s) REWARD([r,s]) h 2 BLACK(s) (r=J) REWARD([r,s]) h 3 ([r,s]=[4,C]) ([r,s]=[7,C]) [r,s]=[2,S]) REWARD([r,s]) h 4 ([r,s]=[5,H]) ([r,s]=[J,S]) REWARD([r,s]) agree with all the examples in the training set
30. Multiple Inductive Hypotheses h 1 NUM(r) BLACK(s) REWARD([r,s]) h 2 BLACK(s) (r=J) REWARD([r,s]) h 3 ([r,s]=[4,C]) ([r,s]=[7,C]) [r,s]=[2,S]) REWARD([r,s]) h 4 ([r,s]=[5,H]) ([r,s]=[J,S]) REWARD([r,s]) agree with all the examples in the training set Need for a system of preferences – called an inductive bias – to compare possible hypotheses
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36. Supervised Learning Flow Chart Training set Target concept Datapoints Inductive Hypothesis Prediction Learner Hypothesis space Choice of learning algorithm Unknown concept we want to approximate Observations we have seen Test set Observations we will see in the future Better quantities to assess performance
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