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Notable titles in Machine Learning
Delve into the transformative world of Machine Learning with our curated collection of essential titles. Explore fundamental concepts, algorithms, and techniques used to build intelligent systems and analyze data. Understand key topics such as supervised and unsupervised learning, neural networks, and deep learning. Discover the latest advancements and applications in machine learning across various industries. Our selection is perfect for data scientists, engineers, and enthusiasts who want to stay ahead in the rapidly evolving field of machine learning. From theoretical foundations to practical implementations, these comprehensive resources will enhance your understanding and skills in developing cutting-edge machine learning solutions.
Featured Titles
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Machine Learning in Pure Mathematics and Theoretical Physics Machine Learning in Pure Mathematics and Theoretical Physics
edited by Yang-Hui He (London Institute for Mathematical Sciences, UK & Merton College, University of Oxford, UK & City, University of London, UK & Nankai University, China)

The terms "machine learning", "pure mathematics and theoretical physics" may first appear to be topics in two very different categories in mathematics and computer science, yet in the past five years, we have seen many articles published that combined machine learning techniques in the search for patterns using big data. The aim of this book, a first of its kind, is to collect research and survey articles from many experts in this emerging collaboration between theoretical mathematicians and computer scientists on topics ranging from combinatorics to number theory, to geometry, to quantum field theory and string theory.
Machine Learning with Python Machine Learning with Python
Theory and Applications
by G R Liu (University of Cincinnati, USA)

Machine Learning (ML) has become a very important area of research widely used in various industries.

This compendium introduces the basic concepts, fundamental theories and essential computational techniques related to ML models. With most essential basics and a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.
Machine Learning Machine Learning
Concepts, Tools and Data Visualization
by Minsoo Kang (Eulji University, South Korea) & Eunsoo Choi (All4Land Inc., South Korea)

This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided...
Principles of Quantum Artificial Intelligence Principles of Quantum Artificial Intelligence
Quantum Problem Solving and Machine Learning
2nd Edition
by Andreas Wichert (Instituto Superior Técnico - Universidade de Lisboa, Portugal & INESC-ID, Portugal)

This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.
Handbook on Big Data and Machine Learning in the Physical Sciences Handbook on Big Data and Machine Learning in the Physical Sciences
(In 2 Volumes)
editor-in-chief Sergei V Kalinin (Oak Ridge National Laboratory, USA) & Ian Foster (Argonne National Laboratory, USA & University of Chicago, USA), edited by Surya Kalidindi (GaTech, USA), Sergei V Kalinin (Oak Ridge National Laboratory, USA), Turab Lookman (Los Alamos National Laboratory, USA), Kerstin Kleese van Dam (Brookhaven National Laboratory, USA), Kevin G Yager (Brookhaven National Laboratory, USA), Stuart I Campbell (Brookhaven National Laboratory, USA), Richard Farnsworth (Brookhaven National Laboratory, USA) & Maartje van Dam (Brookhaven National Laboratory, USA)

This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.
Highlights
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Machine Learning for Malware Detection
Machine Learning for Malware Detection
edited by Edward Raff (Booz Allen Hamilton, USA) & Charles K Nicholas (University of Maryland, Baltimore County, USA)

Biological Pattern Discovery with R
Biological Pattern Discovery with R
Machine Learning Approaches
by Zheng Rong Yang (University of Exeter, UK)

Statistical Machine Learning with Applications in Finance
Statistical Machine Learning with Applications in Finance
by Gordon Ritter (Columbia University, USA & New York University, USA & Baruch College, USA)

Python, Data Science and Machine Learning
Python, Data Science and Machine Learning
From Scratch to Productivity
by Paul Alexander Bilokon (Thalesians Ltd, UK)

Machine Learning — A Journey to Deep Learning
Machine Learning — A Journey to Deep Learning
with Exercises and Answers
by Andreas Wichert (Instituto Superior Técnico — Universidade de Lisboa, Portugal & INESC-ID, Portugal) & Luis Sa-Couto (Instituto Superior Técnico — Universidade de Lisboa, Portugal & INESC-ID, Portugal)

Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning
Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning
(In 4 Volumes)
edited by Cheng Few Lee (Rutgers University, USA) & John C Lee (Center for PBBEF Research, USA)

An Introduction to Machine Learning in Quantitative Finance
An Introduction to Machine Learning in Quantitative Finance
by Hao Ni (University College London, UK), Xin Dong (Citadel Securities LLC, UK), Jinsong Zheng (Huatai Securities, China) & Guangxi Yu (SWS Research, China)

Mathematical Analysis for Machine Learning and Data Mining
Mathematical Analysis for Machine Learning and Data Mining
by Dan Simovici (University of Massachusetts, Boston, USA)

Introduction to Pattern Recognition and Machine Learning
Introduction to Pattern Recognition and Machine Learning
by M Narasimha Murty (Indian Institute of Science, India) & V Susheela Devi (Indian Institute of Science, India)

Intrusion Detection
Intrusion Detection
A Machine Learning Approach
by Zhenwei Yu (University of Illinois, Chicago, USA) & Jeffrey J P Tsai (University of Illinois, Chicago, USA & Asia University, Taiwan)

Linear Algebra and Optimization with Applications to Machine Learning
Linear Algebra and Optimization with Applications to Machine Learning
Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning
by Jean Gallier (University of Pennsylvania, USA) & Jocelyn Quaintance (University of Pennsylvania, USA)

Linear Algebra and Optimization with Applications to Machine Learning
Linear Algebra and Optimization with Applications to Machine Learning
Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning
by Jean Gallier (University of Pennsylvania, USA) & Jocelyn Quaintance (University of Pennsylvania, USA)

Mathematical Analysis for Machine Learning and Data Mining
Mathematical Analysis for Machine Learning and Data Mining
by Dan Simovici (University of Massachusetts, Boston, USA)

Introduction to Pattern Recognition and Machine Learning
Introduction to Pattern Recognition and Machine Learning
by M Narasimha Murty (Indian Institute of Science, India) & V Susheela Devi (Indian Institute of Science, India)

Intrusion Detection
Intrusion Detection
A Machine Learning Approach
by Zhenwei Yu (University of Illinois, Chicago, USA) & Jeffrey J P Tsai (University of Illinois, Chicago, USA & Asia University, Taiwan)

Machine Learning for Financial Engineering
Machine Learning for Financial Engineering
edited by László Györfi (Budapest University of Technology and Economics, Hungary), György Ottucsák (Budapest University of Technology and Economics, Hungary) & Harro Walk (Universität Stuttgart, Germany)

Applied Health Care Analytics
Applied Health Care Analytics
Enabling Transformative Health Care through Data Science, Machine Learning, and Cognitive Computing
by Mark Albert (Loyola University, USA), Plamen Petrov (Deloitte Consulting, USA) & Rajeev Ronanki (Deloitte Consulting, USA)

Journal Articles of Interest
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International Journal of Pattern Recognition and Artificial Intelligence International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

Top papers for IJPRAI