Machine Learning Algorithms for Problem Solving in Computational Applications

Why solving the information problem should be at the core of our pandemic response: essential reading about the long-term implications of our current crisis. COVID-19 is caused by a virus. The COVID-1

An accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas.Digital technology runs on algorithms, sets of instr

An introduction to computational thinking that traces a genealogy beginning centuries before the digital computer.A few decades into the digital era, scientists discovered that thinking in terms of co

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.This book offers a comprehensive introduction to optimization with a focus on pr

The gap between theoretical ideas and messy reality, as seen in Neal Stephenson, Adam Smith, and Star Trek.We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execut

When organizations apply old methods of problem-solving to new kinds of problems, they may accomplish only temporary fixes or some ineffectual tinkering around the edges. Today's problems are a new br

This introduction to quantum algorithms is concise but comprehensive, covering many key algorithms. It is mathematically rigorous but requires minimal background and assumes no knowledge of quantum th

In this book, Marcin Milkowski argues that the mind can be explained computationally because it is itself computational -- whether it engages in mental arithmetic, parses natural language, or processe

Seeing has puzzled scientists and philosophers for centuries and it continues to do so. This new edition of a classic text offers an accessible but rigorous introduction to the computational approach

This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interaction

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of

The use of innovative computational models in political economic research as a complement to traditional analytical methodologies. Researchers are increasingly turning to computational methods to st

This title covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of

Chapters focus on four interrelated areas: applications and algorithms, device technology, architecture and systems, and software technology. Building a computer ten times more powerful than all the

Reflecting Alan Robinson's fundamental contribution to computational logic, this book brings together seminal papers in inference, equality theories, and logic programming. It is an exceptional collec

Digital Halftoning addresses the problem of developing algorithms that best match the specific parameters of any target display device. It is the first significant study of the process of producing qu

As the contributions to this book make clear, a fundamental change is taking place in the study of computational linguistics analogous to that which has taken place in the study of computer vision ove

A cognitive ethnography of how bioengineering scientists create innovative modeling methods.In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Ner

A guide to both theory and practice of blended learning offering rigorous research, case studies, and methods for the assessment of educational effectiveness.Blended learning combines traditional in-p

An argument for a Copernican revolution in our consideration of mental features—a shift in which the world-brain problem supersedes the mind-body problem.Philosophers have long debated the mind-body p

An introduction to the quantitative modeling of biological processes, presenting modeling approaches, methodology, practical algorithms, software tools, and examples of current research.The quantitati

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data

The new edition of a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models.This book offers students and researchers a guide to dist

Upon its publication by the MIT Press in 1972, Learning from Las Vegas was immediately influential and controversial. The authors made an argument that was revolutionary for its time -- that the bill

In 1969, ten years after the discovery of the perceptron -- which showed that a machine could be taught to perform certain tasks using examples -- Marvin Minsky and Seymour Papert published Perceptro

We depend on -- we believe in -- algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, a

"Machine art" is neither a movement nor a genre, but encompasses diverse ways in which artists engage with technical systems. In this book, Andreas Broeckmann examines a

This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using

The goal of structured prediction is to build machine learning models that predictrelational information that itself has structure, such as being composed of multiple interrelatedparts. These models,

In this book, Dan Gusfield examines combinatorial algorithms to constructgenealogical and exact phylogenetic networks, particularly ancestral recombination graphs (ARGs).The algorithms produce network

Long ago, in 1985, personal computers came in two general categories: the friendly,childish game machine used for fun (exemplified by Atari and Commodore products); and the boring,beige adult box used

The nature of the interplay between language learning and the evolution of a language over generational time is subtle. We can observe the learning of language by children and marvel at the phenomeno

Policy makers need quantitative as well as qualitative answers to pressing policy questions. Because of advances in computational methods, quantitative estimates are now derived from coherent nonline

The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explan

The nature of the interplay between language learning and the evolution of a language over generational time is subtle. We can observe the learning of language by children and marvel at the phenomenon

Introducing a method for solving combinatorial optimisation problems that combines the techniques of constraint programming and local search. The ubiquity of combinatorial optimisation problems in our

This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on

In The Government Machine, Jon Agar traces the mechanization of government work in the United Kingdom from the nineteenth to the early twenty-first century. He argues that this transformation has bee

Researchers are increasingly turning to computational methods to study the dynamic properties of political and economic systems. Politicians, citizens, interest groups, and organizations interact in d

The Rapid Evaluation of Potential Fields in Particle Systems presents a group of algorithms for the computation of the potential and force fields in large-scale systems of particles that are likely to