Historically, medical imaging has been a qualitative or semi-quantitative modality. It is
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …

Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their
careers in deep learning and artificial intelligence research. The other target audience …

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

Abstract Machine learning (ML) has recently gained in popularity, spurred by well-publicized
advances like deep learning and widespread commercial interest in big data analytics …

Deep learning allows computational models that are composed of multiple processing
layers to learn representations of data with multiple levels of abstraction. These methods …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

Learning algorithms related to artificial neural networks and in particular for Deep Learning
may seem to involve many bells and whistles, called hyper-parameters. This chapter is …

The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be
increasingly important as disease modifying agents become available, early in the course of …