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) 2
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) 3
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) 4
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) 5
 Defining Machine Learning

Definition # 1
Arthur Samuel (1959) coined the term machine learning and
defined it as: ‘the field of study that gives computers the
ability to learn without being explicitly programmed.’ This is .
an informal and old definition of machine learning.

These slides are designed to complement the book Machine Learning with Python by Parteek Bhatia, published by Cambridge University
Press.
 Defining Machine Learning

Definition # 2
In 1998, Tom Mitchell redefined the concept of machine
learning as ‘A computer program is said to learn from
experience E with respect to some class of tasks T and .
performance measures P; if its performance at tasks in T, as
measured by P, improves with experience E.’

These slides are designed to complement the book Machine Learning with Python by Parteek Bhatia, published by Cambridge University
Press.
Understanding E, T and P of ML
 In case of prediction of placement of students. Can you identify
E, T P?

 E-Experience: Previous placement record of students

 T-Task: Prediction of placement

 P-Performance: Accuracy with which models predict

These slides are designed to complement the book Machine Learning with Python by Parteek Bhatia, published by Cambridge University
Press.
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) 9
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) 10
 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

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 11
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) 12
Face Recognition
Training examples of a person

Test images

ORL dataset,
AT&T Laboratories, Cambridge UK

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 13
 Regression
 Example: Price of a used
car
 x : car attributes y = wx+w0
y : price
y = g (x | q )
g ( ) model,
q parameters

14
Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)
Regression Applications
 Navigating a car: Angle of the steering
 Kinematics of a robot arm

(x,y) α1= g1(x,y)

α2= g2(x,y)
α2

α1

◼ Response surface design

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 15
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

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 16
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

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 17
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, ...

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 18
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/

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 19
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
 ...
Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 20
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)
 ...

Lecture Notes for E Alpaydın 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0) 21