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Pen and paper exercises in machine learning
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Pen and paper exercises in machine learning 

[Submitted on 27 Jun 2022]

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Abstract: This is a collection of (mostly) pen-and-paper exercises in machine learning.
The exercises are on the following topics: linear algebra, optimisation,
directed graphical models, undirected graphical models, expressive power of
graphical models, factor graphs and message passing, inference for hidden
Markov models, model-based learning (including ICA and unnormalised models),
sampling and Monte-Carlo integration, and variational inference.

Submission history

From: Michael Gutmann [view email]

[v1]
Mon, 27 Jun 2022 16:53:18 UTC (1,679 KB)

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