Machine Learning Applications In Software Engineering

This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based t

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday

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

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past d

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.Today, machine learning underlies a range of app

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.Today many information source

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk a

How science is opening up the mysteries of the heart, revealing the poetry in motion within the machine.Your heart is a miracle in motion, a marvel of construction unsurpassed by any human-made creation. It beats 100,000 times every day―if you were to live to 100, that would be more than 3 billion beats across your lifespan. Despite decades of effort in labs all over the world, we have not yet been able to replicate the heart’s perfect engineering. But, as Sian Harding shows us in The Exquisite Machine, new scientific developments are opening up the mysteries of the heart. And this explosion of new science―ultrafast imaging, gene editing, stem cells, artificial intelligence, and advanced sub-light microscopy―has crucial, real-world consequences for health and well-being. Harding―a world leader in cardiac research―explores the relation between the emotions and heart function, reporting that the heart not only responds to our emotions, it creates them as well. The condition known as Brok

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictiv

Techniques for applying mathematical concepts in the real world: six rarely taught but crucial tools for analysis, research, and problem-solving.Many young graduates leave school with a solid knowledge of mathematical concepts but struggle to apply these concepts in practice. Real scientific and engineering problems are different from those found in textbooks: they are messier, take longer to solve, and standard solution recipes might not apply. This book fills the gap between what is taught in the typical college curriculum and what a practicing engineer or scientist needs to know. It presents six powerful tools for analysis, research, and problem-solving in the real world: dimensional analysis, limiting cases, symmetry, scaling, making order of magnitude estimates, and the method of successive approximations. The book does not focus on formulaic manipulations of equations, but emphasizes analysis and explores connections between the equations and the application. Each chapter introdu

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comp

After completing this self-contained course on server-based Internet applications software, students who start with only the knowledge of how to write and debug a computer program will have learned h

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

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

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 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 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

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

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

Today, computers are part of kids' everyday lives, used both for play and for learning. We envy children's natural affinity for computers, the ease with which they click in and out of digital worlds.

Today, computers are part of kids' everyday lives, used both for play and forlearning. We envy children's natural affinity for computers, the ease with which they click in andout of digital worlds. Th

As the power of computing has grown over the past few decades, the field of machinelearning has advanced rapidly in both theory and practice. Machine learning methods are usuallybased on the assumptio

To many science and engineering students, the task of writing may seem irrelevant to their future professional careers. At MIT, however, students discover that writing about their technical work is i

The evolution of the concept of mind in cognitive science over the past 25 years creates new ways to think about the interaction of people and computers. New ideas about embodiment, metaphor as a fun

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

As Apollo 11's Lunar Module descended toward the moon under automatic control, aprogram alarm in the guidance computer's software nearly caused a mission abort. Neil Armstrongresponded by switching of

This text is the first comprehensive presentation of reduction semantics in one volume; it also introduces the first reliable and easy-to-use tool set for such forms of semantics. Software engineers

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

Behind the lectern stands the professor, deploying course management systems, onlinequizzes, wireless clickers, PowerPoint slides, podcasts, and plagiarism-detection software. In theseats are the stud

Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication.Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but in terms of artificial communication.To do this, we need a concept of communication that can take into account the possibility that a communication partner may be not a human being but an al

The contributors are among the world's leading researchers inautomated reasoning. Their essays cover the theory, software system design, and use of these systems to solve real problems.The primary obj

An accessible explanation of the technologies that enable such popular voice-interactive applications as Alexa, Siri, and Google Assistant.Have you talked to a machine lately? Asked Alexa to play a song, asked Siri to call a friend, asked Google Assistant to make a shopping list? This volume in the MIT Press Essential Knowledge series offers a nontechnical and accessible explanation of the technologies that enable these popular devices. Roberto Pieraccini, drawing on more than thirty years of experience at companies including Bell Labs, IBM, and Google, describes the developments in such fields as artificial intelligence, machine learning, speech recognition, and natural language understanding that allow us to outsource tasks to our ubiquitous virtual assistants. Pieraccini describes the software components that enable spoken communication between humans and computers, and explains why it's so difficult to build machines that understand humans. He explains speech recognition technology