This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text ...

Supervised Learning In this section, we introduce several common supervised learning approaches that appear throughout oncology applications. These algorithms take in a set of features and predict a ...

Our findings suggest that integrating machine learning into traditional statistical methods can provide more accurate and generalizable models for disease risk prediction.

Find out how this structured machine learning roadmap called I-Con could lead to breakthroughs in AI.

Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and ...