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Why “Learn” ?

Machine learning is programming computers to optimize a performance criterion using example data or past experience.
There is no need to “learn” to calculate payroll
Learning is used when:

Human expertise does not exist (navigating on Mars),
Humans are unable to explain their expertise (speech recognition)
Solution changes in time (routing on a computer network)
Solution needs to be adapted to particular cases (user biometrics)

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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What We Talk About When We Talk About“Learning”

Learning general models from a data of particular examples
Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce.
Example in retail: Customer transactions to consumer behavior:

People who bought “Blink” also bought “Outliers” (www.amazon.com)

Build a model that is a good and useful approximation to the data.

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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Data Mining

Retail: Market basket analysis, Customer relationship management (CRM)
Finance: Credit scoring, fraud detection
Manufacturing: Control, robotics, troubleshooting
Medicine: Medical diagnosis
Telecommunications: Spam filters, intrusion detection
Bioinformatics: Motifs, alignment
Web mining: Search engines
...

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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What is Machine Learning?

Optimize a performance criterion using example data or past experience.
Role of Statistics: Inference from a sample
Role of Computer science: Efficient algorithms to

Solve the optimization problem
Representing and evaluating the model for inference

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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Applications

Association
Supervised Learning

Classification
Regression

Unsupervised Learning
Reinforcement Learning

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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Learning Associations

Basket analysis:

P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services.

Example: P ( chips | beer ) = 0.7

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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Classification

Example: Credit scoring
Differentiating between low-risk and high-risk customers from their income and savings

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

Discriminant: IF income > θ₁ AND savings > θ₂

THEN low-risk ELSE high-risk

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Classification: Applications

Aka Pattern recognition
Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style
Character recognition: Different handwriting styles.
Speech recognition: Temporal dependency.
Medical diagnosis: From symptoms to illnesses
Biometrics: Recognition/authentication using physical and/or behavioral characteristics: Face, iris, signature, etc
...

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

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Face Recognition

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

Training examples of a person

Test images

ORL dataset,

AT&T Laboratories, Cambridge UK

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Regression

Example: Price of a used car
x : car attributes

y : price

y = g (x | θ )

g ( ) model,

θ parameters

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

y = wx+w₀

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Regression Applications

Navigating a car: Angle of the steering
Kinematics of a robot arm

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)

α₁= g₁(x,y)

α₂= g₂(x,y)

α₁

α₂

(x,y)

Response surface design

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Supervised Learning: Uses

Prediction of future cases: Use the rule to predict the output for future inputs
Knowledge extraction: The rule is easy to understand
Compression: The rule is simpler than the data it explains
Outlier detection: Exceptions that are not covered by the rule, e.g., fraud

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Unsupervised Learning

Learning “what normally happens”
No output
Clustering: Grouping similar instances
Example applications

Customer segmentation in CRM
Image compression: Color quantization
Bioinformatics: Learning motifs

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Reinforcement Learning

Learning a policy: A sequence of outputs
No supervised output but delayed reward
Credit assignment problem
Game playing
Robot in a maze
Multiple agents, partial observability, ...

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Resources: Datasets

UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html
UCI KDD Archive: http://kdd.ics.uci.edu/summary.data.application.html
Statlib: http://lib.stat.cmu.edu/
Delve: http://www.cs.utoronto.ca/~delve/

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Resources: Journals

Journal of Machine Learning Research www.jmlr.org
Machine Learning
Neural Computation
Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annals of Statistics
Journal of the American Statistical Association
...

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Resources: Conferences

International Conference on Machine Learning (ICML)
European Conference on Machine Learning (ECML)
Neural Information Processing Systems (NIPS)
Uncertainty in Artificial Intelligence (UAI)
Computational Learning Theory (COLT)
International Conference on Artificial Neural Networks (ICANN)
International Conference on AI & Statistics (AISTATS)
International Conference on Pattern Recognition (ICPR)
...