Basics of Machine Learning Series
Supervised Learning
Supervised learning is the machine learning 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 a desired output value (also called the supervisory signal).
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Regression: When the target variable is continuous. Regression in supervised learning is different from regression in statistics. Also, logistic regression is a classification technique despite its name as originally it outputs the probability of belongingness to a class. It is converted in to a classification algorithm by applying a threshold on this probability output by the algorithm to convert it into binary classes.
- Example: Given a picture of a person, predict the age on the basis of the given picture.
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Classification: When the target variable is categorical.
- Example: Given a patient with a tumor, predict whether the tumor is malignant or benign.
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.
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Clustering: Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).
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Applications:
- Organizing large computing clusters
- Social network analysis
- Market segmentation
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Astronomical data analysis
- Cocktail Party Algorithm: It is an example of source seperation algorithm or Independent Component Analysis (ICA). It can be considered to be opposite of clustering in some sense.
REFERENCES:
Supervised Learning: Wikipedia
Machine Learning: Coursera - Supervised Learning
Unsupervised Learning: Mathworks
Machine Learning: Coursera - Unsupervised Learning
Cluster Analysis: Wikipedia