Machine learning is a process where machines or rather, computer code running on machines, is created that allows the code to develop its own methods to categorise information based on data that we feed into it. Scientists at the University of Oxford are working on ways to improve the speed and accuracy of these systems. In the future, many decisions are likely to be made, not by people, but by computer algorithms. These algorithms might be used for a range of different ‘tests’ from recognising faces to diagnosing medical conditions. It might be possible in the future to run far more tests quickly and cheaply. However, is this always desirable?
In this lesson, students discover some of the uses of machine learning in addition to exploring the numbers behind false negatives and false positives and some of their surprising consequences.
- Students can give examples of machine learning
- Students can describe what is meant by a false positive and a false negative and solve simple problems involving them
- Students recognize that false positives/negatives can lead to counterintuitive results
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