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+

This book takes the creativity and inventiveness of the maker movement and applies that energy in a new way to help children learn across all subject areas as well as broaden their world view. Traditional library literacy programs have helped many children foster a love of reading, but to prepare this next generation of learners, this programming needs to be modified to include technology. The inherent creativity and inventiveness of the Maker Movement, embracing both classic and innovative technological activities, provides the perfect bridge to invigorate, expand, and update these programs. This alternative to conventional library literacy programming will help children learn throughout all subject areas, see additional possibilities, and make connections in the world around them. With this guide, readers can discover how to apply maker literacy to introduce connections that help children better understand that their experiences in life are interrelated—that art can be made on a 3D printer and that science and technology are an essential part of design. This holistic approach provides a myriad of creative opportunities for both teaching staff and the children they serve. A great resource for youth services librarians in public libraries, this guide to infusing library programs with technology and maker activities to motivate learning will also appeal to preschool and elementary librarians, educators, and parents.

People currently live in a digital age in which technology is now a ubiquitous part of society. It has become imperative to develop and maintain a comprehensive understanding of emerging innovations and technologies. Information and Technology Literacy: Concepts, Methodologies, Tools, and Applications is an authoritative reference source for the latest scholarly research on techniques, trends, and opportunities within the areas of digital literacy. Highlighting a wide range of topics and concepts such as social media, professional development, and educational applications, this multi-volume book is ideally designed for academics, technology developers, researchers, students, practitioners, and professionals interested in the importance of understanding technological innovations.

Lynne Bowker and Jairo Buitrago Ciro introduce the concept of machine translation literacy, a new kind of literacy for scholars and librarians in the digital age. This book is a must-read for researchers and information professionals eager to maximize the global reach and impact of any form of scholarly work.

With the recent explosion of technology into the world of education across the globe, this book sets out a framework for rethinking the three key areas of schooling that are most affected by technology's impact on education today: knowledge as curriculum; learning and pedagogy and literacy across the curriculum. A well-known author in this field, Jewitt takes the reader through an analysis of teaching and learning with materials such as CD-ROMs, websites, the Internet, computer programming applications and computer games, relating each in turn to the main curriculum topics. Through this detailed scrutiny the following questions emerge: How do the new technologies reshape knowledge as curriculum? How does the use of new technologies in the classroom reshape learning pedagogy? As writing moves from page to screen, what is the impact on students' situated literacy practices and how does it effect learning? Through these questions, this book demonstrates that mode, technology and curriculum knowledge are fundamentally connected and describes how teacher and student roles in the classroom could be altered in the face of new technologies.

"This book discusses the efficacy of instructional technology in various, global learning environments"--Provided by publisher.

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, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.