Pen and Paper Exercises in Machine Learning
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.
PDF AbstractResults from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.