这本书《Machine Learning: A Physicist Perspective》由Nelson Bolivar撰写,主要从物理学家的角度探讨了机器学习的原理和应用。这本书旨在为包括物理学家、机器学习专家、学生和教授在内的广大读者提供一个全面的机器学习基础介绍,同时也为研究人员提供了深入理解机器学习原理的高级理论视角。 ### 第1章:机器学习基础...
Physics, too, has fallen into the artificial intelligence hype with a clutch of researchers using machine learning to deal with complex problems regarding huge amount of data.
They even named their network in honor of Ludwig Boltzmann, the physicist whose work formed the foundation of statistical mechanics. And the connection between neural networks and physics isn’t a one-way street. Machine learning was crucial to the discovery of the Higgs boson, where it sorted ...
Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning
For image recognition that requires special expertise, machine learning can provide even bigger benefits. "These techniques are extremely efficient at finding subtle signals" like small shifts in particle tracks, said Gabe Perdue, a Fermilab physicist on the MINERvA experiment. ...
“The Disk Detective science team has been working on its own machine-learning project, and now that this paper is out, we’re going to have to get together and compare notes,” says Marc Kuchner, a senior astrophysicist at NASA’s Goddard Space Flight Center and leader of the crowdsourcin...
报告人简介:Dr. Yi Zhang is a theoretical condensed matter physicist. He obtained his undergraduate degree at Fudan University and his Ph.D. at UC Berkeley under advisor Prof. Ashvin Vishwanath. Afterward, Dr. Zhang joined Stanford University as an SITP postdoctoral fellow and later Cornell Univers...
many seemingly complex natural phenomena are governed by simple and elegant mathematical laws such as partial differential equations. Stephen Wolfram, the creator of Mathematica, computer scientist, and physicist, makes the following observation: “It turns out that almost all the traditional math...
The slow and uneven forging of a novel constellation of practices, concerns, and values that became machine learning occurred in 1950s and 1960s pattern recognition research through attempts to mechanize contextual significance that involved building “l
An accelerator physicist by training, Daniel Ratner has worked to apply machine learning approaches to accelerators at SLAC for many years and now heads up the initiative. In this Q&A, he discusses what machine learning can do and how SLAC is uniquely equipped to advance the use of machine lea...