Currently, more than 30 million people in the world have speech impairments and thus to communicate have to use sign language resulting in a language barrier between sign language and non-sign language users. This project explores the development of a sign language to speech translation glove by implementing a Support Vector Machine(SVM) on the Intel Edison to recognize various letters signed by sign language users. The data for the predicted signed gesture is then transmitted to an Android application where it is vocalized. The sign language glove has five flex sensors mounted on each finger to quantify how much a finger is bent. Flex sensors are sensors that change their resistance depending on the amount of bend on the sensor.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. The book explains this'why'; that is, it explains the purpose and progress of statistical research, through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.

As readers of my blog will know, I want to learn more about machine learning. I've managed to run some samples, and I've built my own first little samples. It feels like the next step is to understand more about the different algorithms, for example when to pick which one and how to tune the parameters to achieve the best results. To learn more, I've started to watch the first hours of the awesome courses below. The courses are a great introduction to machine learning and very different from most other videos I found which often seem to assume you are already a data scientist.

When a very young child looks at a picture, she can identify simple elements: "cat," "book," "chair." Now, computers are getting smart enough to do that too. In a thrilling talk, computer vision expert Fei-Fei Li describes the state of the art -- including the database of 15 million photos her team built to "teach" a computer to understand pictures -- and the key insights yet to come. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more.

How important are skills in computational thinking for computing app constructors and for computing users in general? If we can teach our children early on to smile, talk, write, read, and count through frequent and repetitive use of patterns in well-chosen examples, is it also possible for us, assuming we have the skills, to teach our children to construct computing applications? Do we need to first teach them anything about computational thinking before we look to teach how to construct computing apps? If not, how important will computational skills be for us all, as Jeannette Wing suggests in her blog@cacm "Computational Thinking, 10 Years Later" (Mar. Many competent and successful computing app constructors and users never hear a word about computational thinking but still manage to acquire sufficient construction and user skills through frequent and repetitive use of patterns in well-chosen examples.

That is what I said when I was asked whether we would ever see computer science taught in K–12. It was 2009, and I was addressing a gathering of attendees to a workshop on computational thinking (http://bit.ly/1NjmcRJ) It has been 10 years since I published my three-page "Computational Thinking" Viewpoint (http://bit.ly/1W73ekv) in the March 2006 issue of Communications. To celebrate its anniversary, let us consider how far we've come. Since the dotcom bust, there had been a steep and steady decline in undergraduate enrollments in computer science, with no end in sight.

The ability to make continual, accurate decisions based on evolving data is key in many of today's data-driven intelligent systems. This tutorial-style talk presents an introduction to the modern study of sequential learning and decision making under uncertainty. The broad objective is to cover modeling frameworks for online prediction and learning, explore algorithms for decision making, and gain an understanding of their performance. Specifically, we will look at multi-armed bandits- models of decision making that capture the explore-vs-exploit tradeoff in learning, regret minimization, non-stochastic or adversarial online learning, and online convex optimization. Time permitting, we will discuss new directions and frontiers in the area of sequential decision making.

The AI analyzes student answers, and groups them based on similarity.

Virtual patients are becoming a useful tool for medical students and a resource for medical schools. The obvious reality that students can make mistakes with no risk to the "patient" is part of the attraction to this technology. "Virtual patients allow students to learn without putting real patients at risk," said Norm Berman, MD, professor of pediatrics at the Geisel School of Medicine at Dartmouth and the lead author of a perspective piece recently published by the journal Academic Medicine. "No actual patients are harmed in the process of learning from virtual patients." The authors outlined the role of virtual patients in relation to the challenges and opportunities within medical education.