Machine Learning for Scientific Data Analysis

Abstract Over the last few years, machine learning has revolutionized countless areas and fields. Nowadays, AI bears promise for analyzing, extracting knowledge, driving discovery across many scientific domains such as chemistry, biology, genomics. However, specific challenges posed by data demand to adapt techniques new requirements. We investigate learning-driven analysis, focusing on a set of key These include management uncertainty complex models, estimation system properties starting from low-volume imprecise collected data, support model development through large-scale analysis experimental integration complementary technologies.