The age of touch could soon come to an end. From smartphones and smartwatches, to home devices, in-car systems, touch is no longer the primary user interface. During this talk, Christophe, Principal Consultant at GraphAware will walk you through the design of building Conversational Bots. To this end, he used Amazon Alexa and combined it with a Natural Language Processing stack backed by a Neo4j Graph Database. You will discover the basics of an Amazon Alexa skill and how the user experience with voice devices can be enhanced with graph based algorithms such as recommendations.

A new tool that could allow us to easily identify fake online profiles has been developed. Researchers trained computer models to spot Pornhub users who made up information about themselves, commonly known as catfish. The system is designed to identify people who are dishonest about their age or gender by studying their writing style and how they comment on posts. It works with more than 90 per cent accuracy, and scientists believe it could help to ensure the safety of social networks. Researchers trained computer models to spot users of Pornhub who made up information about themselves, commonly known as catfish.

For example, some Indian researchers used CRF to extract key words from medical texts and they had good features and large enough training sample, but they obtained quality not more than 0.4 (F1-measure). On real data they would hardly obtain such quality, while Stanford NER shows quality not more than 0.81 (F-measure) given it has perfectly selected training features and it was trained on larger corpora (CoNLL, MUC-6, MUC-7 and ACE) Some Spanish and Russian researchers compared HMM and CRF in NER task for medical texts on JNLPBA corpus (18546 sentences with 109588 named entities). They obtained interesting results: HMM had higher recall ( 4-7% depending on the type of entity) while CRF had higher precision ( 4-13% depending on the type of entity). According to one master thesis, linear-chain CRF operated very well on extracting time expressions from Russian text.

A new program called Project Icarus is teaching kids how to build drones, while at the same time helping them grow in STEM fields, which focus in science, technology, engineering and mathematics. Project Icarus, launched by the Texas-based Flynoceros Racing Drones, is a workshop that not only allows kids to build drones, but also teaches them how to fix and fly them. This is different from drone kits bought at stores that don't show users how repair them. Seven-year-old Ava builds her drone at a Project Icarus workshop. Although parents can buy a drone kit at a retail store, those products are sometimes cheap and can break easily after crashing.

We hear the term "machine learning" a lot these days, usually in the context of predictive analysis and artificial intelligence. Machine learning is, more or less, a way for computers to learn things without being specifically programmed. But how does that actually happen? The answer is, in one word, algorithms. Algorithms are sets of rules that a computer is able to follow.

This course is about the fundamental concepts of machine learning, focusing on neural networks, SVM and decision trees. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. The first chapter is about regression: very easy yet very powerful and widely used machine learning technique.

As a machine learning & AI engineer, you would: - Develop of server-side applications using machine learning and artificial intelligence techniques. Benefits: - Be able to work in an international creative environment, have the opportunity to develop personal capacity with competitive salary.

When someone commits suicide, their family and friends can be left with the heartbreaking and answerless question of what they could have done differently. Colin Walsh, data scientist at Vanderbilt University Medical Center, hopes his work in predicting suicide risk will give people the opportunity to ask "what can I do?" while there's still a chance to intervene. Walsh and his colleagues have created machine-learning algorithms that predict, with unnerving accuracy, the likelihood that a patient will attempt suicide. In trials, results have been 80-90% accurate when predicting whether someone will attempt suicide within the next two years, and 92% accurate in predicting whether someone will attempt suicide within the next week. The prediction is based on data that's widely available from all hospital admissions, including age, gender, zip codes, medications, and prior diagnoses.

Machine learning is transforming many areas of computer science. From natural language processing and search to computer music, many systems that were once made with symbolic systems are now hybrids of symbolic and statistical machinery. This shift presents new challenges and opportunities for learning about computer science, for studying learning about computing, and for collaborating with researchers from other education research disciplines. In this workshop, we will collaboratively define a research agenda for CS Education Research on the topic of machine learning. Participants will give brief presentations about the ideas in their position papers (see below), then form groups to discuss and further develop related ideas.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.