Pen and Paper Exercises in Machine Learning by Michael Gutmann

This is a collection of (mostly) pen-and-paper exercises in machine learning. Each exercise comes with a detailed solution. The following topics are covered:
- 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
- Variational Inference

Link: GitHub

Navigational hashtags: #armrepo
General hashtags: #math #mathematics #linearalgebra