This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

Machine Learning (ML) algorithms have revolutionized various domains by enabling data-driven decision-making and automation.

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

In recent years, machine learning (ML) algorithms have proved themselves to be remarkably useful in helping people deal with different tasks: data classification and clustering, pattern revealing, ...

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area We trained and validated two-phase ML ...

Machine learning is a rapidly growing field with endless potential applications. In the next few years, we will see machine learning transform many industries, including manufacturing, retail and ...

Metabolite data and AI combine to redefine how we measure aging and predict health spans. Study: Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning ...