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ML 3.ppt
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) 2 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) 3 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) 4 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) 5 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) 6
7.
CLUSTERING: Lecture Notes for
E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 7
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) 8
9.
Differentiation: Lecture Notes for
E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 9
10.
Lecture Notes for
E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 10 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) 11
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