Parametric & Non-Parametric Machine Learning (Supervised ML)

This slide gives a basic introduction to Parametric & Non-Parametric Supervised Machine Learning.

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