Academic Articles & Research Papers

Bioinformatics the machine learning approach

In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who nee...

Machine Learning with R

Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of...

Machine learning: Trends, perspectives, and prospects

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and a...

Machine learning: a primer

Machine Learning for Sociology

Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and soci...

Automatic Differentiation in Machine Learning: a Survey

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives of numeric functions expressed as computer programs efficiently and acc...

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process a...

Quantum machine learning

Machine learning and data mining

Machine learning algorithms enable discovery of important "regularities" in large data sets....

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniq...

Overfitting and undercomputing in machine learning

suggests a reasonable line of research: find algorithms that can search the hypothesis class better. Hence, there is been extensive research in applying second-order methods to fit neural networks and in conducting much more thorough search...

Machine learning for molecular and materials science

Machine Learning

An Introduction to Machine Learning

Machine learning for microbiologists

Pretreatment serum IRAK4 and NLRP3 concentrations, their combined score, and overall survival in non-small cell lung cancer: a retrospective single-center cohort study.

Incidence and risk factors for rotator cuff retear after arthroscopic rotator cuff repair in middle-aged and older patients: a systematic review and meta-analysis.

Molecular mechanisms of dust-induced lung disease: exploring the role of physicochemical composition and cellular pathways.

Metabolic-extracellular matrix crosstalk in hepatocellular carcinoma: mechanisms and targeted intervention.

Air pollution is associated with a distinct pathogen spectrum in community-acquired pneumonia: a prospective multicentre cohort study.