Inside The Teaching Machine

Gayatri Chakravorty Spivak is one of the most pre-eminent postcolonial theorists writing today and a scholar of genuinely global reputation. This collection, first published in 1993, presents some of Spivak’s most engaging essays on works of literature such as Salman Rushdie's controversial Satanic Verses, and twentieth century thinkers such as Jacques Derrida and Karl Marx. Spivak relentlessly questions and deconstructs power structures where ever they operate. In doing so, she provides a voice for those who can not speak, proving that the true work of resistance takes place in the margins, Outside in the Teaching Machine.

How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Advocates of higher education have long contended that universities should operate above the crude material negotiations of economics and politics. Such arguments, ignore the historical reality that the American university system emerged through, and in service to, a capitalist political economy that unevenly combines corporate, state, and civil interests. As the corporatization of U.S. universities becomes nearly impossible to deny, the common response from many academics has been a superior stand against the contamination of the professional ideal by tainted corporate interests. Inside the Teaching Machine proposes a correction to this view through the lens of historical materialism. Chaput argues that the U.S. public research university has always been a vital component of the capitalist political economy. While conventional narratives of public higher education emphasize civic preparation and upward mobility, Chaput demonstrates that supposedly egalitarian policies like the Morrill Land-Grant Act and the G.I. Bill served the changing interests of capitalism much as education, creating a professional class that supports the capitalist political economy. Chaput also focuses on the relationship between American universities and globalization, showing how the trend toward professionalization contributes to the production of surplus value, and the ways that the American university model circulates outside the United States. Chaput concludes by advocating rhetorical strategies for the professional who opposes the capitalist logic of the global university system, proposing concrete options for engaging and redirecting globalization within the university system.

Technology promises to make learning better, cheaper, faster—but rarely has it kept that promise. The allure of educational technology is easy to understand. Classroom instruction is an expensive and time-consuming process fraught with contradictory theories and frustratingly uneven results. Educators, inspired by machines’ contributions to modern life, have been using technology to facilitate teaching for centuries. In Teaching Machines, Bill Ferster examines past attempts to automate instruction from the earliest use of the postal service for distance education to the current maelstrom surrounding Massive Open Online Courses. He tells the stories of the entrepreneurs and visionaries who, beginning in the colonial era, developed and promoted various instructional technologies. Ferster touches on a wide range of attempts to enhance the classroom experience with machines, from hornbooks, the Chautauqua movement, and correspondence courses to B. F. Skinner’s teaching machine, intelligent tutoring systems, and eLearning. The famed progressive teachers, researchers, and administrators that the book highlights often overcame substantial hurdles to implement their ideas, but not all of them succeeded in improving the quality of education. Teaching Machines provides invaluable new insight into our current debate over the efficacy of educational technology.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+

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 new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.