  Uncertainty plays a crucial role in the machine learning field. Both model
trustworthiness and performance require the understanding of uncertainty,
especially for models used in high-stake applications where errors can cause
cataclysmic consequences, such as medical diagnosis and autonomous driving.
Accordingly, uncertainty decomposition and quantification have attracted more
and more attention in recent years. This short report aims to demystify the
notion of uncertainty decomposition through an introduction to two types of
uncertainty and several decomposition exemplars, including maximum likelihood
estimation, Gaussian processes, deep neural network, and ensemble learning. In
the end, cross connections to other topics in this seminar and two conclusions
are provided.
