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Machine Learning
An Introduction
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What is Learning? Herbert Simon: “Learning is any process by
which a system improves performance from experience.”
What is the task? Classification Problem solving / planning / control
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Classification Assign object/event to one of a given finite set of
categories. Medical diagnosis Credit card applications or transactions Fraud detection in e-commerce Worm detection in network packets Spam filtering in email Recommended articles in a newspaper Recommended books, movies, music, or jokes Financial investments DNA sequences Spoken words Handwritten letters Astronomical images
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Problem Solving / Planning / Control Performing actions in an environment in
order to achieve a goal. Solving calculus problems Playing checkers, chess, or backgammon Balancing a pole Driving a car or a jeep Flying a plane, helicopter, or rocket Controlling an elevator Controlling a character in a video game Controlling a mobile robot
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Learning from DataThe world is driven by data.
Germany’s climate research centre generates 10 petabytes per year Google processes 24 petabytes per day The Large Hadron Collider produces 60 gigabytes per minute (~12
DVDs) There are over 50m credit card transactions a day in the US alone.
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Learning from Data
Data is recorded from some real-world phenomenon. What might we want to do with that data?
Prediction - what can we predict about this phenomenon?
Description - how can we describe/understand this phenomenon in a
new way?
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Learning from Data
How can we extract knowledge from data to help humans take decisions?
How can we automate decisions from data?
How can we adapt systems dynamically to enable better user experiences?
Write code to explicitlydo the above tasks
Write code to make the computerlearn how to do the tasks
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Using machine learning to detect spam emails
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ALGORITHMNaïve BayesRule mining
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Using machine learning to recommend books
ALGORITHMSCollaborative FilteringNearest NeighbourClustering
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Using machine learning to identify faces and expressions
ALGORITHMSDecision Trees
Adaboost
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Boundary Detection
Is this a boundary?
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Image CategorizationTraining Labels
Training Images
Classifier
Training
Training
Image Features
Image Features
Testing
Test Image
Trained
Classifier
Trained Classifier Outdoor
Prediction
Slide: Derek Hoiem
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ML for working with social network data ML for working with social network data: detecting
fraud, predicting click-thru patterns, targeted advertising, etc etc etc .
ALGORITHMSSupport Vector MachinesCollaborative filteringRule mining algorithmsMany many more….
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ML for mobile
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Machine Learning (etc.) Driving a car Recognising spam emails Recommending books Reading handwriting Recognising speech, faces, etc.
How would you write these programs?Would you want to?!?!?!?
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Machine Learning Problems