Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

The authors devise an efficient quantum approach to address the van der Waals interactions due to photoexcitations by approximating the Bethe-Salpeter equation. Both attractive/repulsive forces can ...

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels ...

Catalog description: Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of ...

In the largest experiment studying people's "risky choices" to date, researchers show how machine learning can be used to test and improve long-stagnant theories of human decision-making.

In the machine learning world, the sizes of artificial neural networks — and their outsize successes — are creating conceptual conundrums. When a network named AlexNet won an annual image recognition ...

We are excited to inform you that the current Machine Learning: Theory and Hands-On Practice with Python Specialization (taught by Professor Geena Kim) is being retired and will be replaced with a new ...

The recognition is for a 2005 paper titled “Agnostically Learning Halfspaces,” which Klivans co-authored with Adam Tauman ...

Who is the Master's in Artificial Intelligence and Machine Learning program for? Drexel’s College of Computing & Informatics' Master of Science in Artificial Intelligence and Machine Learning (MSAIML) ...