Physics of Data Science and Machine Learning

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A01=Ijaz A. Rauf
advanced data analysis
AI
ANOVA Model
artificial intelligence
Author_Ijaz A. Rauf
Category=PH
Category=UY
Category=UYQ
computational physics
Cyberphysical Systems
data science
Data's Statistical Characteristics
Data’s Statistical Characteristics
Decision Optimization Model
Digital Twin
digital twin technology
EL Equation
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
experimental design
Full Factorial Design
Generalized Coordinates
Hidden Units
Independent Generalized Coordinates
Lagrangian Mechanics
machine learning
Ml Model
neural networks
Null Hypothesis
optimization methods
Orthogonal Arrays
Physical Experiment Data
probabilistic modeling
quantum
Quantum Field Theory
Quantum Mechanics
quantum mechanics for machine learning
Regression Models
Response Surface Methodology
Slack Parameters
statistical mechanics
Support Vector Machines
Unlabeled Data
Vice Versa
Virtual Displacement
Wave Function

Product details

  • ISBN 9780367768584
  • Weight: 430g
  • Dimensions: 156 x 234mm
  • Publication Date: 29 Nov 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work.

This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, while exploring neural networks and machine learning, building on fundamental concepts of statistical and quantum mechanics.

This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence.

Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools.

Key Features:

  • Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt.
  • Free from endless derivations; instead, equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand.
  • Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts.

Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an associate researcher at Ryerson University, Toronto, Canada and president of the Eminent-Tech Corporation, Bradford, ON, Canada.

Ijaz A. Rauf is Adjunct Professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an Associate Researcher at Ryerson University, Toronto, Canada and President of the Eminent-Tech Corporation, Bradford, ON, Canada.

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