Combinatorial Machine Learning

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides ne

The book focusses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are use

This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the

This book provides frequently studied and used machines together with soft computing methods such as evolutionary computation. The main topics of the machine learning cover Artificial Neural Networks

"This book provides the information on the machine learning techniques that can be in business problems in efficient way. It encourages the data Scientists to take a proactive attitude toward applying

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies

This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the

The new edition of this popular, undergraduate textbook will be revised and updated to reflect current growth areas in Machine Learning. The new edition will include three new chapters with more detai

A new, intuitive approach to machine learning, covering fundamental concepts and real-world applications, with practical MATLAB-based exercises.

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and n

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deployin

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender

Wan and Mak describe machine-learning approaches to determining automatically the location of a protein within a cell in order to facilitate drug discovery and drug design. The approaches exploit the

The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not onl

Apply a fully test-driven approach to machine-learning algorithms, and save yourself the pain of missing mistakes in your analyses. Most data scientists have run an analysis and simply accepted any an

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern rep

Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible t

This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book fr

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and

"This book discusses detection of anomalies in computer networks from a machine learning perspective. It introduces readers to how computer networks work and how they can be attacked by intruders in s

Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM."Structural Health Monitoring: A Machine

This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Author Manas A. Pathak presents solutions