Machine Learning Applications In Software Engineering

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

The applications of hydrodynamics to naval architecture and marine engineering expanded dramatically in the 1960s and 1970s. This classic textbook, originally published in 1977, filled the need for a

Why human skills and expertise, not technical tools, are what make projects succeed.The project is the basic unit of work in many industries. Software applications, antiviral vaccines, launch-ready spacecraft: all were produced by a team and managed as a project. Project management emphasizes control, processes, and tools―but, according to The Smart Mission, that is not the right way to run a project. Human skills and expertise, not technical tools, are what make projects successful. Projects run on knowledge. This paradigm-shifting book―by three project management experts, all of whom have decades of experience at NASA and elsewhere―challenges the conventional wisdom on project management, focusing on the human dimension: learning, collaboration, teaming, communication, and culture. The authors emphasize three themes: projects are fundamentally about how teams work and learn together to get things done; the local level―not an organization’s upper levels―is where the action happens; an

Experts translate the latest findings on embodied cognition from neuroscience, psychology, and cognitive science to inform teaching and learning pedagogy.Embodied cognition represents a radical shift in conceptualizing cognitive processes, in which cognition develops through mind-body environmental interaction. If this supposition is correct, then the conventional style of instruction―in which students sit at desks, passively receiving information―needs rethinking. Movement Matters considers the educational implications of an embodied account of cognition, describing the latest research applications from neuroscience, psychology, and cognitive science and demonstrating their relevance for teaching and learning pedagogy. The contributors cover a range of content areas, explaining how the principles of embodied cognition can be applied in classroom settings. After a discussion of the philosophical and theoretical underpinnings of embodied cognition, contributors describe its applications

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

A comprehensive introduction to the field of autonomous robotics aimed at upper-level undergraduates and offering additional online resources.Textbooks that provide a broad algorithmic perspective on the mechanics and dynamics of robots almost unfailingly serve students at the graduate level. Introduction to Autonomous Robots offers a much-needed resource for teaching third- and fourth-year undergraduates the computational fundamentals behind the design and control of autonomous robots. The authors use a class-tested and accessible approach to present progressive, step-by-step development concepts, alongside a wide range of real-world examples and fundamental concepts in mechanisms, sensing and actuation, computation, and uncertainty. Throughout, the authors balance the impact of hardware (mechanism, sensor, actuator) and software (algorithms) in teaching robot autonomy. Features:Rigorous and tested in the classroomWritten for engineering and computer science undergraduates with a soph

An introduction to neurorobotics that presents approaches and design principles for developing intelligent autonomous systems grounded in biology and neuroscience.Neurorobotics is an interdisciplinary field that draws on artificial intelligence, cognitive sciences, computer science, engineering, psychology, neuroscience, and robotics. Because the brain is closely coupled to the body and situated in the environment, neurorobots―autonomous systems modeled after some aspect of the brain―offer a powerful tool for studying neural function and may also be a means for developing autonomous systems with intelligence that rivals that of biological organisms. This textbook introduces approaches and design principles for developing intelligent autonomous systems grounded in biology and neuroscience. It is written for anyone interested in learning about this topic and can be used in cognitive robotics courses for students in psychology, cognitive science, and computer science.Neurorobotics covers

Over the last half century, "smartness"―the drive for ubiquitous computing―has become a mandate: a new mode of managing and governing politics, economics, and the environment.Smart phones. Smart cars. Smart homes. Smart cities. The imperative to make our world ever smarter in the face of increasingly complex challenges raises several questions: What is this "smartness mandate?" How has it emerged, and what does it say about our evolving way of understanding―and managing―reality? How have we come to see the planet and its denizens first and foremost as data-collecting instruments? In The Smartness Mandate, Orit Halpern and Robert Mitchell radically suggest that "smartness" is not primarily a technology, but rather an epistemology. Through this lens, they offer a critical exploration of the practices, technologies, and subjects that such an understanding relies upon―above all, artificial intelligence and machine learning. The authors approach these not simply as techniques for solving pr

The entanglements of people and data, code and space, knowledge and power: how data and algorithms shape the world―and shape us within that world.With the emergence of a post-truth world, we have witnessed the dissolution of the common ground on which truth claims were negotiated, individual agency enacted, and public spheres shaped. What happens when, as Nietzsche claimed, there are no facts, but only interpretations? In this book, Mark Shepard examines the entanglements of people and data, code and space, knowledge and power that have produced an uncommon ground―a disaggregated public sphere where the extraction of behavioral data and their subsequent processing and sale have led to the emergence of micropublics of ever-finer granularity. Shepard explores how these new post-truth territories are propagated through machine learning systems and social networks, which shape the public and private spaces of everyday life. He traces the balkanization and proliferation of online news and t

The study of type systems for programming languages now touches many areas of computer science, from language design and implementation to software engineering, network security, databases, and analy

Machine learning develops intelligent computer systems that are able to generalizefrom previously seen examples. A new domain of machine learning, in which the prediction mustsatisfy the additional co

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. Machine learning develops intelligent computer systems that are able to genera

In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a s

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inv

In this thought-provoking study of work, worker, and machine in the postindustrial age, Hirschhorn points out that factories will become places of learning where the worker must be able to diagnose an