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 ...

In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning ...

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 ...

Heterogeneous catalysis underpins a wide variety of industrial processes including energy conversion, chemical manufacturing and environmental remediation. Significant advances in computational ...

The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations.

The biggest thing is the community. UT is warm, welcoming, and genuinely homey, and that kind of environment matters more ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

In the table below, we show the main strength, tuition and program length for the best machine learning bootcamps. In this ...

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 ...