Introduction to Machine Learning EM algorithm
Prof. Kutty
Generative models
Copyright By PowCoder代写加微信 assignmentchef
Gaussian Mixture Model (GMM)
Mixture of Gaussians
image source: Bishop 2006
MLE of GMM with known labels: Example
1.4 -0.625
2.1, 0, 3.5, −1, 1.5, 2.5, −0.5, 0.05,1, −2, 0, 1, −2, 1.1, −0.5, −0.03
Log-Likelihood for GMMs with known labels
!”! =$%((̅”,*(“))
Sso f Maximum log likelihood objective
=$%((̅”|*(“))%(*(“)) ! (“#$
=$-. / 0)(1 (̅(“) 2̅(‘),3′))4’) “#$ ‘#$
ln# $! =ln&'( ) *)(- /̅(“) 0̅(%),2%))3%)
“#$ %#$ !&
=”() *)ln($!%&'(#)(&(!),*!%)) “#$ %#$
Gaussian Mixture Model (GMM) Model Parameters
How many independent model parameters in a mixture of 4 spherical Gaussians?
use this link for in-class exercises
https://forms.gle/jqAdK1sSMhcx6zDHA
spherical Gaussian R e c a l l P d f o f ( j ) 2
1 (j) 2 2 2 | | x ̄ μ ̄ g | |
P(x ̄|μ ̄ , )= N
D e l l a j
( 2 ⇡ j2 ) d / 2
Gaussian Mixture Model (GMM) Model Parameters
How many independent model parameters in a mixture of 4 spherical Gaussians?
use this link for in-class exercises
https://forms.gle/jqAdK1sSMhcx6zDHA