Pen and Paper Exercises in Machine Learning
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.
# link_41w48lg3r6.pdf ## Metadata - PDFFormatVersion=1.5 - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - CreationDate=D:20220628011136Z - Creator=LaTeX with hyperref - ModDate=D:20220628011136Z - Custom.PTEX.Fullbanner=This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2 - Producer=pdfTeX-1.40.21 - Trapped=False ## Contents ### Page 1 Pen & PaperExercises in Machine LearningMichael U. GutmannUniversity of EdinburgharXiv:2206.13446v1 [cs.LG] 27 Jun 2022 ### Page 2 This work is li
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