论文标题
钢笔和纸练习在机器学习中
Pen and Paper Exercises in Machine Learning
论文作者
论文摘要
这是机器学习中（主要是）笔和纸练习的集合。这些练习在以下主题上：线性代数，优化，有向图形模型，无向图形模型，图形模型的表达能力，因子图和消息传递，隐藏马尔可夫模型的推断，基于模型的学习（包括ICA和非正态模型），采样和Monte-Carlo集成和各种推力。
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.
