Future epidemics are inevitable and it takes months and even years to collect fully annotated
data. The sheer magnitude of data required for machine learning algorithms spanning both
shallow and deep structures raises a fundamental question: how big data is big enough to
effectively tackle future epidemics? In this context active learning often referred to as
human or expert-in-the-loop learning becomes imperative enabling machines to commence
learning from day one with minimal labeled data. In unsupervised learning the focus shifts
toward constructing advanced machine learning models like deep structured networks that
autonomously learn over time with human or expert intervention only when errors occur and for
limited data-a process we term mentoring. In the context of Covid-19 this book explores the
use of deep features to classify data into two clusters (0 1: Covid-19 non-Covid-19) across
three distinct datasets: cough sound Computed Tomography (CT) scan and chest x-ray (CXR). Not
to be confused our primary objective is to provide a strong assertion on how active learning
could potentially be used to predict disease from any upcoming epidemics. Upon request
(education training purpose) GitHub source codes are provided.
