The book also examines computer and digital media access and the scientific basis for the theory and practice of sensory substitution. They discuss technology for multiple applications (mobility, wayfinding, information access, education, work, entertainment), including both established technology and cutting-edge research. Written by world-class leaders in their field, the chapters cover the physiological bases of vision loss and the fundamentals of orientation, mobility, and information access for blind and low vision individuals. Presenting the current state of the art, this book emphasizes what can be learned from past successful products, as well as what exciting new solutions the future holds. Assistive Technology for Blindness and Low Vision explores a broad range of technologies that are improving the lives of these individuals. The key factor is to create consumer-driven technologies that solve the problems by addressing the needs of persons with visual impairments.

The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics by graduate students in disciplines with applications for predictive data analytics and as a reference for professionals.Download Assistive Technology for Blindness and Low Vision Book in PDF, Epub and KindleĪssistive technology has made it feasible for individuals with a wide range of impairments to engage in many activities, such as education and employment, in ways not previously possible.

Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.Īfter discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. 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. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. Machine learning is often used to build predictive models by extracting patterns from large datasets. Kelleher, Brian Mac Namee & Aoife D'ArcyĪ comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Title: Fundamentals of Machine Learning for Predictive Data Analytics.

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