dr. mary-angela papalaskari department of computing ...map/4510/01intro.pdf · • the world is...
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CSC 4510 – Machine Learning
Dr. Mary-Angela Papalaskari Department of Computing Sciences Villanova University
Course website: www.csc.villanova.edu/~map/4510/
Introduction
CSC 4510 - M.A. Papalaskari - Villanova University
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Machine Learning
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What is Learning?
• Herbert Simon (1970): “Learning is any process by which a system improves performance from experience.”
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What is Machine Learning?
• Arthur Samuel (1959): Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
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Why Study Machine Learning? Engineering Better Computing Systems
• Develop systems that are too difficult/expensive to program explicitly because they require specific detailed skills or knowledge tuned to a specific task – Personalized news or mail filter – Personalized tutoring
SPAM CSC 4510 - M.A. Papalaskari - Villanova University
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Why Study Machine Learning? Cognitive Science
• Computational studies of learning may help us understand learning in humans and other biological organisms. – Hebbian neural learning
• “Neurons that fire together, wire together.” – Human’s relative difficulty of learning disjunctive
concepts vs. conjunctive ones. – Power law of practice
log(# training trials)
log(
perf.
tim
e)
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Why Study Machine Learning? The time is ripe
• Large amounts of computational resources available. • Many basic effective and efficient algorithms available. • The world is driven by data (data mining).
– Market basket analysis (e.g. diapers and dvds) – News aggregation – Over 50m credit card transactions a day in the US alone. – The Large Hadron Collider produces 60 gigabytes per minute – Climate research centres generate 1-20 petabytes per year – Google processes 24 petabytes per day
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So, um, what’s a petabyte again?
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Humans can: - think, learn, see, understand language, reason, etc.
Artificial Intelligence aims to reproduce these capabilities. Machine Learning is one part of Artificial Intelligence.
Artificial Intelligence Statistics / Mathematics
Computer Vision
Data Mining
Machine Learning
Robotics
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Let’s try something
• You will be given instructions in class to collect data about your classmates
• Enter these data in the document provided • We will use the decision tree algorithm
from aispace.org/ to “learn” something about your sample
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Next time
• Some historical background on AI and a more careful definition of machine learning
• Discussion of Alan Turing article: “Computing Machinery and Intelligence” http://loebner.net/Prizef/TuringArticle.html
• See also: • Alan Turing website maintained by Andrew Hodges:
http://www.turing.org.uk/turing/ • Philosophical objections to Turing Test
http://plato.stanford.edu/entries/chinese-room/
Some of the slides in this presentation are adapted from: • Prof. Frank Klassner’s ML class at Villanova • the University of Manchester ML course http://www.cs.manchester.ac.uk/ugt/COMP24111/ • The Stanford online ML course http://www.ml-class.org/ CSC 4510 - M.A. Papalaskari - Villanova University