FAU Lecture Notes in Pattern Recognition

On Hidden Information and How to Estimate it…

The Expectation Maximization Algorithm

Andreas Maier
CodeX
Published in
13 min readApr 26, 2021

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Image under CC BY 4.0 from the Pattern Recognition Lecture

These are the lecture notes for FAU’s YouTube Lecture “Pattern Recognition”. This is a full transcript of the lecture video & matching slides. The sources for the slides are available here. We hope, you enjoy this as much as the videos. This transcript was almost entirely machine generated using AutoBlog and only minor manual modifications were performed. If you spot mistakes, please let us know!

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Welcome back to Pattern Recognition! Today we want to look a bit more into the EM algorithm. In particular, we want to learn how to apply it to other problems. We will start with the so-called missing information principle.

Image under CC BY 4.0 from the Pattern Recognition Lecture

Now the missing information principle is as simple as that the observable information is given as the complete information minus the hidden information.

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Andreas Maier
CodeX
Writer for

I do research in Machine Learning. My positions include being Prof @FAU_Germany, President @DataDonors, and Board Member for Science & Technology @TimeMachineEU