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Pen and Paper Exercises in Machine Learning

2022/06/27 by Michael U. Gutmann, Gutmann, Michael U. · 25 voices
Computer Science · #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2206.13446

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.

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