- Supervised Learning
Unsupervised Learning
Supervised Learning and Unsupervised Learning.png
Supervised Learning
"Right Answers“ given; Predict continuous output.
Already know that is the data set of 'correct answers' that we would like that algorithms have predict on that data set.
Regression Problem
Predict continuous valued output (e.g., price)
Classification Problem
Discrete valued output (0 or 1) (or more than two values)
A represents a linear classification problem and B represents a non-linear classification problem.
About infinite number of features?
A learning algorithm that might deal with an infinite number of features.
So how to deal with an infinite number of features? And how to store an infinite number of attributes?
The answer is Support Vector Machine
Support Vector Machine
There will be a neat mathematical trick that will allow a computer to deal with an infinite number of features.
Unsupervised Learning
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.
The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
Given a data set, we're not told what to do with it and we're not told each data point is. Instead, we just find some structure in the data.
Given this data set, an Unsupervised Learning algorithm might decide that the data lives in two different clusters.
Unsupervised Learning algorithm may break these data into these two separate clusters. So this is called a clustering algorithm.
Examples of Unsupervised Learning Algorithm
- Organize computing clusters
- Social network analysis
- Market segmentation
- Astronomical data analysis
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Cocktail party problem (/effect)
Cocktail Party Problem.png
Cocktail party problem algorithm with Octave programming environment
[W,s,v] = svd ( ( repmat ( sum ( x. * x, 1 ), size ( x, 1), 1 ). * x ) * x' );
