The document explores various types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. It emphasizes that supervised learning involves analyzing labeled training data to generate a function for classifying new instances, while unsupervised learning works with unlabeled data to discover hidden structures. Key applications and distinctions between these learning types, such as classification and regression in supervised learning and clustering in unsupervised learning, are also highlighted.

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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