This document provides an overview of parametric and non-parametric supervised machine learning. Parametric learning uses a fixed number of parameters and makes strong assumptions about the data, while non-parametric learning uses a flexible number of parameters that grows with more data, making fewer assumptions. Common examples of parametric models include linear regression and logistic regression, while non-parametric examples include K-nearest neighbors, decision trees, and neural networks. The document also briefly discusses calculating parameters using ordinary least mean square for parametric models and the limitations when data does not follow predefined assumptions.

1. PARAMETRIC & NON-
PARAMETRIC SUPERVISED
MACHINE LEARNING
Rehan Guha
Senior Machine Learning Researcher
Pramati Technologies Pvt. Ltd.

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5. TYPES OF MACHINE
LEARNING

11. PARAMETRIC SUPERVISED LEARNING
A parametric algorithm has a fixed number of parameters.
A parametric algorithm is computationally faster, but makes stronger
assumptions about the data; the algorithm may work well if the
assumptions turn out to be correct, but it may perform badly if the
assumptions are wrong.
A learning model that summarises data with a set of parameters of fixed
size (predefined mapped function) (independent of the number of
training examples). No matter how much data you throw at a parametric
model, it won’t change its mind about how many parameters it needs.
A common example of a parametric algorithm is Linear Regression,
Linear Support Vector Machines, Perceptron, Logistic Regression.

12. y= m.x + b

13. HOW TO CALCULATE “M” OR
“GRADIENT” ?
Ordinary Least Mean Square

14. “What if Data does not follow the pre-
defined algorithm?

15. NON-PARAMETRIC SUPERVISED LEARNING
In contrast, a non-parametric algorithm uses a flexible number of
parameters, and the number of parameters often grows as it learns
from more data.
A non-parametric algorithm is computationally slower, but
makes fewer assumptions about the data.
Non-parametric methods are good when you have a lot of data and no
prior knowledge, and when you don’t want to worry too much about
choosing just the right features.
A common example of a non-parametric algorithm is K-nearest
neighbour, Decision Trees, Artificial Neural Networks, Support
Vector Machines with Gaussian Kernels.

17. THE END