Gaussian Mixture Model (GMM): Introduction [E12]

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In this video, I have given an introduction to the Gaussian Mixture Model, I talked about the Maximum likelihood, which in the next lecture will be elaborated and then we will find the parameters mean , covariance matrix and the pi-ks

Notes link:
  1. Pratik Jain
  2. Gaussian Mixture Model
  3. GMM
  4. Introduction
  5. Pattern Recognition
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Комментарии
Автор

very clean explanation . Thanks Pratik.

RAJANKUMAR-miib
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Thank you Pratik for an amazing explanation

mowlanicabilla
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bro the main formula which you said - Probability of the test sample to fall under the particular gaussian. How do we get that? I mean, is there any method to derive it or we just have the formula and the formula remains same no matter how many gaussian curves appear??

Thoda btao

dipankarnandi
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Hi Pratik. The P(x) is the joint probability right? Why did you say it is the likelihood. Can you please explain how to interpret P(x)?

sudeshnadutta
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thanks for such a nice explanation..please suggest me some platform where i can solve numerical on machine learning

mdzeeshan