Overview Machine learning offers efficiency at scale, but trust depends on understanding how decisions are madeAs machine ...

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, ...

Ben Khalesi covers the intersection of artificial intelligence and everyday tech at Android Police. With a background in AI and data science, he enjoys making technical topics approachable for those ...

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models is learning without crossing ethical lines.

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

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 ...

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 ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

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 ...

AI algorithms, trained on historical data reflecting men's sports dominance, may be gatekeeping sports content on social ...