Machine Learning & its Applications

 SUBTITLE: FACULTY DEVELOPMENT PROGRAM

 DATE: 6/3/23 to 11/3/23


VENUE: ZEAL POLYTECHNIC PUNE.

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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
 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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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
Examples
 Spam filter is a Machine Learning program that can learn to flag
spam given examples of spam emails (e.g., flagged by users) and
examples of regular (non spam, also called “ham”)emails.
 The examples that the system uses to learn are called the training
set. Each training example is called a training instance(or
sample).
 In this case, the task T is to flag spam for new emails, the
experience E is the training data, and the performance measure P
needs to be defined; for example, you can use the ratio of
correctly classified emails.
 This particular performance measure is called accuracy and it is
often used in classification tasks.
 Applications
 Association
 Supervised Learning
 Classification
 Regression
 Unsupervised Learning
 Reinforcement Learning

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

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

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

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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
 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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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
 Regression
 Example: Price of a used car
 x : car attributes

y : price y = wx+w0
y = g (x | θ)
g ( ) model,
θ parameters

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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
 Regression Applications
 Navigating a car: Angle of the steering wheel (CMU NavLab)
 Kinematics of a robot arm

(x,y) α1= g1(x,y)

α2= g2(x,y)
α2

α1

 Response surface design

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