Explore how machine learning impacts nanoscience and nanotechnology, leading to breakthroughs in material science, diagnostics, and energy solutions.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

This leads to improvements in complex-system simulations or tasks that involve multiple variables in quantum machine learning, making it ideal for applications across various scientific disciplines.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

Compared to classical algorithms, quantum machine learning demonstrates significant advantages in feature extraction, model training, and predictive inference.

Discover the power of machine learning to analyze and make predictions on huge data, improve decision-making and enhance automation processes.

On April 10-14, the five participating universities of the Institute for Data, Econometrics, Algorithms, and Learning hosted a workshop examining multiple areas of interpretability.

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 ...

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.