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Lecture 7 : Intro to Machine Learning ,[object Object],[object Object]
Reminders ,[object Object],[object Object],[object Object]
Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Where is ML useful? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning agents
Learning element ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning terms ,[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object]
Inductive Learning ,[object Object],[object Object],[object Object],Occam’s razor: prefer the simplest hypothesis consistent with the data
Induction Task Example ,[object Object],[object Object],[object Object]
Induction Task Example
Decision Trees : PlayTennis
Decision Trees ,[object Object],[object Object],[object Object],[object Object]
Expressiveness ,[object Object],[object Object],[object Object],[object Object]
Hypothesis spaces ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision tree learning ,[object Object],[object Object]
Choosing an attribute ,[object Object],[object Object]
Learning decision trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Attribute-based representations ,[object Object],[object Object],[object Object],Exercise : Build a consistent decision tree from the examples
Decision trees ,[object Object],[object Object]
Entropy ,[object Object],[object Object],[object Object],[object Object]
Information gain ,[object Object],[object Object],[object Object],[object Object]
Information gain ,[object Object],[object Object],[object Object]
Example contd. ,[object Object],[object Object]
Build a decision tree ,[object Object]
Solution tree
Performance measurement ,[object Object],[object Object],[object Object],[object Object],[object Object]
Question ,[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Learning ,[object Object],[object Object],[object Object],[object Object]
Bayes’ Theorem ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bayesian Exercises ,[object Object],[object Object],[object Object],[object Object]
Bayesian Learning Example
Bayesian Learning Curve for Example
Bayesian Learning Example
Naive Bayesian classifier ,[object Object],[object Object],[object Object],[object Object]
Classifying reviews: Changeling ,[object Object],[object Object]
How to Classify? ,[object Object],[object Object],[object Object]
How to classify ,[object Object],[object Object]
Evaluating Your Classifier ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Readings ,[object Object]

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Lecture 7

Notas do Editor

  1. Most important part of a ML system is the learning element. Spam filter: learning element has figured out which words indicate something is spam. Usually starts from some training set, adjusted by “report spam” button.
  2. Explain basic example of training and testing sets Explain basic idea of a classifier
  3. The algorithm may not return the same tree, but it will return a tree that is logically equivalent, assuming that the method for generating the training set eventually generates all possible combinations of attributes. For example, if the method picks the value of each attribute uniformly at random, the probability that it generates all possible combinations goes to one when the training set goes to infinity. The form of the tree may not be the same, because there are multiple ways of representing the same logical function.
  4. Go over terminology on the board
  5. 1)P(C | +) = P(+|C)*P(C) / P(+) = P(+|C)*P(C) / [P(+|C)*P(C) + P(+|~C)*P(~C)] (.98*.005)/(.98*.005+.02*.995) = 0.1976 2) P(Second|First) = P(First and Second) / P(First) = 0.25/0.42 = 0.60 = 60%
  6. Go over: -pickling -dictionaries