Lecture 6.2: Probabilistic PCA

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In this video, we introduce Latent Variable Models. As the first model, we consider the Probabilistic Principal Component Analysis (pPCA). We can treat the pPCA as a shallow neural network with a single linear layer, and a model whether all distributions are Gaussian.
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At 9:41, on the right hand side of C^{-1}, the first term should have \sigma raised to the (-) second power, \sigma^{-2} I, in order for the product of C and C-inverse to be I.

maxwellsdaemon