Название: Physics of Data Science and Machine Learning
Автор: Ijaz A. Rauf
Издательство: CRC Press
Год: 2022
Страниц: 211
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

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, whilst 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 the development of new and innovative Machine Learning and Artificial Intelligence tools.

Machine Learning is emerging as an umbrella noun for methods studied and developed for many decades in different scientific communities, such as statistical learning, pattern recognition, image processing and analysis, computer vision, and computational learning, among many others. A machine processes the data and automatically finds structures in the data, i.e. learns. The knowledge about the extracted structure in the data is then used to solve the problems at hand. This approach of problem-solving is called inductive. Machine learning is about inductively solving problems by machines, i.e. computers. The ability to provide accurate predictions and forecasts stems from machine learning models by considering real-time input data and historical data. Although an accurate prediction or forecast is invaluable, it is priceless to make analytics-driven decisions about the best course of action to be taken. Artificial Intelligence (AI) is referred to as machines’ ability to mimic the human trait of decision-making based on data analyses and prior knowledge.

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 explained strategically and explain 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.