Introduction to Machine Learning and different types of learning algorithms including supervised, unsupervised, semi-supervised, reinforcement learning etc.

1. ML Types
Supervised, Unsupervised Learning…
PCS 206
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2. Overview
ML Types
Supervised Learning
Unsupervised Learning
Semi supervised Learning
Reinforcement Learning
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3. Machine Learning begins with DATA
• Labeled data
• Unlabeled data
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4. Supervised Learning…Contd.
• A supervised learning algorithm analyzes the training data
• Produces an inferred function
• This function is used for mapping new examples.
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5. Supervised Learning
• (x,y)
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22. Machine Learning Algorithms
• Machine Learning methods are broadly classified into following
categories:
Supervised Learning
Algorithm generates a function that
maps input to desired function
Labelled data
Eg. Classification and regression
Unsupervised learning
No labelled examples are available
Eg. Clustering, association rule mining
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23. Supervised Learning: Training
• Data mining task of inferring a function from labeled training data.
• The training data consist of a set of training examples.
• In supervised learning, each example is a pair consisting of an input object
(typically a vector) and the desired output value.
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24. Supervised Learning…Testing
• An optimal scenario will allow for the algorithm to correctly determine the class
labels for unseen instances.
• This requires the learning algorithm to generalize from the training data to unseen
situations in a “reasonable” way.
• Eg. classification and regression algorithms
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25. Supervised Classification
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26. Unsupervised Learning
• The information used to train is neither classified nor labeled.
• U.L studies how systems can infer a function to describe a hidden structure from
unlabeled data.
• The system doesn’t figure out the right output, but it explores the data and can
differentiate the given input data.
• All clustering algorithms fall under supervised learning.
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