machine_learning_beginner_level_intro.ppt

INTRODUCTION TO
Machine Learning
ETHEM ALPAYDIN
© The MIT Press, 2004
alpaydin@boun.edu.tr
http://www.cmpe.boun.edu.tr/~ethem/i2ml
Lecture Slides for

Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
3
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)

4
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 “Da Vinci Code” also bought “The Five
People You Meet in Heaven” (www.amazon.com)
 Build a model that is a good and useful
approximation to the data.

5
Data Mining
 Retail: Market basket analysis, Customer
relationship management (CRM)
 Finance: Credit scoring, fraud detection
 Manufacturing: Optimization, troubleshooting
 Medicine: Medical diagnosis
 Telecommunications: Quality of service
optimization
 Bioinformatics: Motifs, alignment
 Web mining: Search engines
 ...

6
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

7
Applications
 Association
 Supervised Learning
 Classification
 Regression
 Unsupervised Learning
 Reinforcement Learning

8
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

9
Classification
 Example: Credit
scoring
 Differentiating
between low-risk
and high-risk
customers from
their income and
savings
Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk

10
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.
 Use of a dictionary or the syntax of the language.
 Sensor fusion: Combine multiple modalities; eg, visual (lip
image) and acoustic for speech
 Medical diagnosis: From symptoms to illnesses
 ...

11
Face Recognition
Training examples of a person
Test images
AT&T Laboratories, Cambridge UK
http://www.uk.research.att.com/facedatabase.html

12
Regression
 Example: Price of a
used car
 x : car attributes
y : price
y = g (x | θ)
g ( ) model,
θ parameters
y = wx+w0

13
Regression Applications
 Navigating a car: Angle of the steering wheel (CMU
NavLab)
 Kinematics of a robot arm
α1= g1(x,y)
α2= g2(x,y)
α1
α2
(x,y)
 Response surface design

14
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

15
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

16
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, ...

17
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/

18
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
 ...

19
Resources: Conferences
 International Conference on Machine Learning (ICML)
 ICML05: http://icml.ais.fraunhofer.de/
 European Conference on Machine Learning (ECML)
 ECML05: http://ecmlpkdd05.liacc.up.pt/
 Neural Information Processing Systems (NIPS)
 NIPS05: http://nips.cc/
 Uncertainty in Artificial Intelligence (UAI)
 UAI05: http://www.cs.toronto.edu/uai2005/
 Computational Learning Theory (COLT)
 COLT05: http://learningtheory.org/colt2005/
 International Joint Conference on Artificial Intelligence (IJCAI)
 IJCAI05: http://ijcai05.csd.abdn.ac.uk/
 International Conference on Neural Networks (Europe)
 ICANN05: http://www.ibspan.waw.pl/ICANN-2005/
 ...

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