ML and its types

1. Machine Learning
and its types
By
D.VAMSI
19BQ1A0125

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

3. Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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TYPES
 Supervised Learning
 Classification
 Regression
 Unsupervised Learning
 Reinforcement Learning

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

5. Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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Unsupervised Learning
 Learning “what normally happens”
 No output
 Clustering: Grouping similar instances

6. Schematic demonstration:
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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7. CLUSTERING:
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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8. Association:
 Association rules allow you to establish associations
amongst data objects inside large databases. This
unsupervised technique is about discovering interesting
relationships between variables in large databases. For
example, people that buy a new home most likely to buy
new furniture.
 Other Examples:
 Groups of shopper based on their browsing and
purchasing histories
 Movie group by the rating given by movies viewers
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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9. Differentiation:
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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10. Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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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, ...

11. .
THANK YOU
Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1)
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