Transcript Introduction to Machine Learning

4.30 Machine Learning
Pádraig Cunningham
Machine Learning Group
University College Dublin
2
Outline
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Week 1

Introduction & General Overview of Matrix Decomposition
 Nearest Neighbour Classifiers
 Tutorial
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Week 2: Neural Networks
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Simple Perceptron, Backpropagation
 Other Architectures: Hopfield, Self-Organising Maps
 Tutorial
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Week 3
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Support Vector Machines
 Kernel Methods & Evaluation
 Tutorial
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Week 4
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Decision Trees
 Naïve Bayes
 Tutorial
Intro to ML
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Outline
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Week 5: Ensemble Techniques
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Bagging
Boosting
Tutorial
Coursework
3-4 pieces, 15 hours, Weka & Java
Week 6: Unsupervised Learning
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Hierarchical Clustering
 Other Clustering Algorithms: k-Means, Spectral Clustering
 Tutorial
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Week 7: Dimension Reduction
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Principle Components Analysis, LSI, SVD
 Feature Selection
 Tutorial
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Later
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2 revision tutorials
Intro to ML
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Why Machine Learning
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Recent progress in algorithms and theory
Loads of processing power
Computational power is available Growing flood of
online data
 Amazon
 Google
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3 niches for ML
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Data mining: using historical data to improve decisions
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Software applications that cannot be programmed by hand.
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medical records  medical knowledge
autonomous driving
speech recognition
i.e. weak theory domains.
Self customising programs
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Personalised Newspaper
E-mail filtering
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Data-mining in medical records
Quality Assurance in Maternity Care.
http://svr-www.eng.cam.ac.uk/projects/qamc/qamc.html
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Rule Learning
The QAMC system uses Decision /trees (I think!)
It is also possible to extract rules from data:If
Then
No previous normal delivery, and
Abnormal 2nd Trimester Ultrasound, and
Malpresentation at admission
Probability of Emergency C-Section is 0.6
Over training dat 26/41 = 0.63
Over test data: 12/20 = 0.6
<Rule taken from Machine Learning by Tom Mitchell>
Intro to ML
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Spam Filtering
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For Machine Learning…
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Lots of training data
High dimensionality data (lots of features)
Email is a diverse concept
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Porn, mortgage, religion, cheap drugs…
Work, family, play…
Spam Filtering is a challenge because…
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Arms race: spammers vs filters
False Positives are unacceptable
Spam is a changing concept
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ALVIN
Problems too difficult to
program by hand
Alvin drives at 70mph on
motorways
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Autonomous Vehicles
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DARPA Grand Challenge 2005
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Winner: Stanley from Stanford
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Various modules use ML
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SmartRadio
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Internet-based music radio
Personalised
 Collaborative Recommendation
 Content-Based
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Recommendation
supported by knowledge discovery from log data
supported by feature extraction from sound files
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feature seleciton
refinement
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Smart Radio
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Smart Radio is a web
based client-server
music application
which allows listeners
build, manage and
share music
programmes
The project was set up to look at a possible model for:
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The regulated distribution of music on the web
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A personalised stream of music service
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To provide an architecture and data to test our data mining and collaborative
filtering algorithms
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ML Dimensions
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Lazy v’s Eager
 k-NN
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v’s rule learning
Supervised v’s Unsupervised
Symbolic v’s Sub-symbolic
Intro to ML