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Data Science
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 science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has ne...
Fundamentals of Machine Learning for Predictive Data Analytics, second edition
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 predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Data Science
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 science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has ne...
Selected Writings of John V. Kelleher on Ireland a
A collection of eighteen critical essays and twenty-six translations spanning the career of one of the founding intellects of Irish Studies, the Selected Writings of John V. Kelleher on Ireland and Irish America consists of five accessible sections. The first gathers Kelleher's essays on the most widely known Irish cultural phenomenon--the literary renaissance of the early twentieth century. Part two contains his judicious assessments of Irish literature in its post-Revolutionary phase. The third section includes Kelleher's insightful essays on the experience of the Irish in America. The fourth section contains essays that examine early Irish literature and culture, opening with a benchmark essay for Irish Studies, "Early Irish History and Pseudo-History," which was read at the inaugural meeting of the American Conference for Irish Studies in 1961. The collection concludes with Kelleher's translations and adaptations of poems in Old, Middle, and Modern Irish, illustrating his command of the language at every stage.
Brain Trust
When the cattle-borne sickness known as Mad Cow Disease first appeared in America in 2003, authorities were quick to assure the nation that the outbreak was isolated, quarantined, and posed absolutely no danger to the general public. What we were not told was that the origins of the sickness may already have been here and suspected for a quarter of a century. This illuminating exposé of the threat to our nation's health reveals for the first time how Mad Cow Disease (a.k.a. Bovine Spongiform Encephalopathy) has jumped species, infecting humans in the form of Creutzfeldt-Jakob Disease (CJD), and may be hidden in the enormous increase in the number of Alzheimer's cases since 1979. Detailing t...
Murder Most Rare
Marie Besnard, the "Queen of Poisoners". Nanny Hazel Doss, killer of four husbands, three children, two sisters, and her mother--all to turn a profit. These are just two of the dozens of deadly and determined women who have been overlooked in the popular annals of serial crime--until now. More difficult to apprehend and motivated by more complex issues, female serial killers may be even more lethal and cunning than their male counterparts.
Machine Learning
A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. 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, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger,...
Algorithms
In the tradition of Real World Algorithms: A Beginner's Guide, Panos Louridas is back to introduce algorithms in an accessible manner, utilizing various examples to explain not just what algorithms are but how they work. Digital technology runs on algorithms, sets of instructions that describe how to do something efficiently. Application areas range from search engines to tournament scheduling, DNA sequencing, and machine learning. Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader. Louridas explains not just what algorithms are but also how they work, offering a wide range of examples and keeping mathematics to a minimum.
Introduction to Deep Learning
A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basi...
