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Parametric & Non-Parametric Machine Learning (Supervised ML)

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Rehan Guha
Rehan Guha

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

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PARAMETRIC & NON- PARAMETRIC SUPERVISED MACHINE LEARNING Rehan Guha Senior Machine Learning Researcher Pramati Technologies Pvt. Ltd.
! Pramati: A culture of building the agile enterprise ! Founded in 1998 ! “Product DNA” : Pramati has built and scaled several independent product companies ! Imaginea : Engineering Services wing of Pramati ! WaveMaker: Flagship product ! more than 350 Open source Commits ! Serving from 5 global locations ! Agile methodology ! 13 Home Grown Products ! Over 200 product companies as customers ! Design Exploration Incubation Lab ! M&A’s of leading global products ! more than 23 Open Source Contributions ! Unique products & services
TYPES OF MACHINE LEARNING
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.
y= m.x + b
HOW TO CALCULATE “M” OR “GRADIENT” ? Ordinary Least Mean Square
“What if Data does not follow the pre- defined algorithm?
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.
THE END

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Parametric & Non-Parametric Machine Learning (Supervised ML)

  • 1. PARAMETRIC & NON- PARAMETRIC SUPERVISED MACHINE LEARNING Rehan Guha Senior Machine Learning Researcher Pramati Technologies Pvt. Ltd.
  • 2. ! Pramati: A culture of building the agile enterprise ! Founded in 1998 ! “Product DNA” : Pramati has built and scaled several independent product companies ! Imaginea : Engineering Services wing of Pramati ! WaveMaker: Flagship product ! more than 350 Open source Commits ! Serving from 5 global locations ! Agile methodology ! 13 Home Grown Products ! Over 200 product companies as customers ! Design Exploration Incubation Lab ! M&A’s of leading global products ! more than 23 Open Source Contributions ! Unique products & services
  • 3.
  • 4.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 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.
  • 16.