Download Bayesian Decision Theory

Introduction to Machine Learning
Yazd University, Electrical and Computer Engineering
Department
Course Title: Advanced Software Engineering
By: Mohammad Ali Zare Chahooki
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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)
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In “Learning” …
 Learning
general models from a data of
particular examples
 Data is cheap and abundant (data warehouses); knowledge is
expensive and scarce.
 Example in retail …
People who bought “DaVinci Code” also bought “The Five PeopleYou Meet
in Heaven” (www.amazon.com)
 Build a model that is a good and useful approximation
to the data.
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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
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Applications
 Learning Associations
 Supervised Learning
 Classification
 Regression
 Unsupervised Learning
 Reinforcement Learning
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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 | delester) = 0.7
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Classification
 Example: Credit
scoring
 Differentiating between
low-risk and high-risk
customers from their
income and savings
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Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
Classification: Applications
 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
 ...
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Face Recognition
Training examples of a person
Test images
AT&T Laboratories, Cambridge UK
http://www.uk.research.att.com/facedatabase.html
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Regression
 Example: Price of a used car
 x : car attributes
y : price
y = g (x | θ )
g ( ) model,
θ parameters
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y = wx+w0
Regression Applications
 Navigating a car: Angle of the steering wheel (CMU NavLab)
 Kinematics of a robot arm
(x,y)
α2
α1
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α1= g1(x,y)
α2= g2(x,y)
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
 In supervised
learning, the aim is to …
learn a mapping from the input to an output whose
…
correct values are provided by a supervisor.
 In unsupervised learning, there is no such supervisor
and …
we only have input data.
The aim is to find the regularities in the input.
 One method is clustering where the aim is to find clusters
or groupings of input.
 Other methods like feature reduction and finding
association rules
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Reinforcement Learning
 In some applications, the output of the system is a
sequence of actions.
 In such a case, a single action is not important; what is
important is the policy that is the sequence of correct
actions to reach the goal.
 There is no such thing as the best action in any
intermediate state; …
an action is good if it is part of a good policy.
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Reinforcement Learning
 In such a case, the machine learning program should be
able to …
assess the goodness of policies and …
learn from past good action sequences to be able to
generate a policy.
 Such learning methods are called reinforcement learning
algorithms.
 A good example is game playing where …
a single move by itself is not that important;
it is the sequence of right moves that is good
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Reference
 E. Alpaydın, "Introduction to Machine Learning 2nd edition
Ed.“, MIT Press, (2010)
 E. Alpaydın, "Introduction to Machine Learning 3nd edition
Ed.“, MIT Press, (2014)
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