Table of contents I
1
The ingredients of machine learning
Tasks: the problems that can be solved with machine learning
Models: the output of machine learning
Features: the workhorses of machine learning
2
Binary classification and related tasks
Classification
Scoring and ranking
Class probability estimation
3
Beyond binary classification
Handling more than two classes
Regression
Unsupervised and descriptive learning
Peter Flach (University of Bristol) Machine Learning: Making Sense of Data August 25, 2012 2 / 349
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