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.
arXiv comments
The associated github page is https://github.com/michaelgutmann/ml-pen-and-paper-exercises
Linked by 2 identifiable commenters in 2 comments across 1 thread, from Jun 2022 to Jun 2022. Found in 2 HN submissions from Jun 2022 to Mar 2025. Open submission discussions.
> section 1.6 about the power method Yeah 1.6 is a really cool exercise! https://arxiv.org/pdf/2206.13446.pdf#page=19 It's so cool to see of why this works (as an engineer I learned about power method with handwaving explanation "it works in the limit" but I never knew why it…
Direct link to PDF: https://arxiv.org/pdf/2206.13446.pdf
sedatkView on HNParent context
> section 1.6 about the power method Yeah 1.6 is a really cool exercise! https://arxiv.org/pdf/2206.13446.pdf#page=19 It's so cool to see of why this works (as an engineer I learned about power method with handwaving explanation "it works in the limit" but I never knew why it…
ivan_ahView on HNParent context