Machine learning for physicists :a hands-on approach /

---

"Version: 20231101"--Title page verso.Includes bibliographical references.1. Preliminaries -- 1.1. Python -- 1.2. GitHub library -- 1.3. Datasets2. Introduction -- 2.1. What is machine learning? -- 2.2. Applications of machine learning -- 2.3. Different types of machine learning -- 2.4. Structure of the bookpart I. Supervised learning. 3. Supervised learning -- 3.1. Definitions, notations, and problem statement -- 3.2. Ingredients of supervised learning -- 3.3. Model evaluation and model selection -- 3.4. Summary -- 3.5. Questions4. Neural networks -- 4.1. Introduction to neural networks -- 4.2. Training neural networks -- 4.3. Libraries for working with neural networks -- 4.4. Summary5. Special neural networks -- 5.1. Convolutional neural network (CNN) -- 5.2. Time-series and recurrent neural networks (RNNs) -- 5.3. Graph neural network -- 5.4. Summarypart II. Unsupervised learning. 6. Unsupervised learning -- 6.1. Clustering -- 6.2. Anomaly detection -- 6.3. Dimensionality reduction -- 6.4. Summary7. Generative models -- 7.1. Maximum likelihood estimation -- 7.2. Restricted Boltzmann machines -- 7.3. Generative adversarial networks (GAN) -- 7.4. Summary.This book presents machine learning (ML) concepts with a hands-on approach for physicists. The goal is to both educate and enable a larger part of the community with these skills. This will lead to wider applications of modern ML techniques in physics. Accessible to physical science students, the book assumes a familiarity with statistical physics but little in the way of specialized computer science background. All chapters start with a simple introduction to the basics and the foundations, followed by some examples, and then proceeds to provide concrete examples with associated codes from a GitHub repository. Many of the code examples provided can be used as is or with suitable modification by the students for their own applications.Advanced undergraduate and graduate students in the physical sciences and their lecturers.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Sadegh Raeisi has a background in quantum computing and quantum information science. He completed his MSc at the University of Calgary and his PhD at the Institute for Quantum Computing at the University of Waterloo, as well as a Postdoc at the Max Planck Institute for the Science of Light in Erlangen. He then moved back to his home country and has held a faculty position since 2017. With about 18 years of research experience within the field of quantum computing, Sadegh is probably most recognized for his pioneering works on macroscopic quantumness and algorithmic cooling, including finding the cooling limit of heat-bath algorithmic cooling (HBAC) techniques which was an open problem for a decade, and for inventing the Blind HBAC technique, which is the optimal and practical HBAC technique. Sedighe Raeisi has a background in high-energy physics, nonlinear dynamics and chaotic systems. She holds a PhD from Ferdowsi University of Mashhad where she also worked for two years as a lecturer after graduation. Her areas of expertise include machine learning and deep learning with special focus on natural language processing (NLP), machine vision, graph neural networks and time series forecasting. She is currently working as a data scientist in the research and development division of Iran's largest telecommunications company.Title from PDF title page (viewed on December 1, 2023).

---

Availability

No copy data

Detail Information

Series Title

-

Call Number

-

Publisher

: .,

Collation

1 online resource (various pagings) :illustrations (some color).

Language

English

ISBN/ISSN

9780750349574

Classification

530.0285

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Physics
Machine learning.
Artificial Intelligence.
COMPUTERS / Artificial Intelligence / General.

Specific Detail Info

-

Statement of Responsibility

Sadegh Raeisi, Sedighe Raeisi.

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment