Combinatorial Machine Learning

Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Googl

This book constitutes the refereed proceedings of the IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2018, held in Hamburg, G

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related.

This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduct

This book provides a practical, hands-on introduction to the use of R in machine learning. Both supervised and unsupervised learning are comprehensively covered in the book, and the book explores key

This book is for teachers interested in incorporating interaction online into their teaching.Interaction Online is a valuable resource for anyone who wants to incorporate an aspect of online interaction in their language teaching. It is relevant for use with online, blended or face-to-face courses and appropriate for a wide range of teachers and learning contexts. This handbook contains over 75 tried and tested activities, the majority of which can be carried out either synchronously or asynchronously. Activities are purposeful and foster interaction between and among learners and instructors, rather than between learner and machine, and make use of generic tools and applications, such as discussion forums, instant message services and Facebook.

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems

Control your machine learning algorithms using test-driven development to achieve quantifiable milestonesAbout This BookBuild smart extensions to pre-existing features at work that can help maximize t

Efficient Learning Machines explores all the major topics of machine learning, including big data, knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and gami

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost e

Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is

This graduate-level textbook introduces fundamental concepts and methods in machinelearning. It describes several important modern algorithms, provides the theoretical underpinningsof these algorithms

The interplay between optimization and machine learning is one of the most importantdevelopments in modern computational science. Optimization formulations and methods are proving tobe vital in design

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research fie

Contributors from natural language processing, machine learning, knowledge representation and engineering, and user interface design explore theories and practices by which people can learn simply by

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

A detailed examination of learning classifier systems (LCS), a form of machine learning system, which incorporates both Evolutionary Algorithms and Reinforcement Learning Algorithms.

A self-study textbook for readers with the mathematical background of a sophomore student of engineering or science. Describes various forms of representing information, and establishes that both reco

This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical persp

This monograph presents a visually striking exploration of Refik Anadol’s data-driven installation, created for the opening of the new BRUSK museum in Bruges. The book offers exclusive visuals and reflections on the intersection of art, technology, and memory in Anadol’s work.Working at the intersection of art, science, and technology, Refik Anadol is best known for his large-scale immersive installations and public artworks that use machine learning, AI, and big data to visualize complex systems. Anadol’s work transforms raw data – ranging from urban landscapes to brainwaves – into dynamic, multi-sensory experiences, pushing the boundaries of how we perceive space, memory, and reality.For the inaugural programme at BRUSK, the new museum for contemporary art in Bruges, Belgium, Anadol presents a major site-specific installation that immerses visitors in a datadriven landscape unique to the city.

Operations Research uses a balanced combination of theory, applications and computations to help you learn the basics of operating research (OR). It focuses on algorithmic and practical implementation of OR techniques. Easy-to-understand numerical examples explain often difficult math concepts, helping you grasp the foundational idea without getting stuck on complex theorems or notations. Full case studies and math-free anecdotes show how algorithms are used in real-life applications.The 11th Edition introduces analytics, artificial intelligence, and machine learning topics that strengthen and streamline the decision-making processes involved in OR. New stories, 3 new chapters, new case studies and sections provide an up-to-date introduction to the field of OR.