Through this challenge Iridescent aims to demystify artificial intelligence through hands-on design challenges and family engagement events across the country. Google will support these events with volunteers and mentors using everyday materials – like rubber bands, paper cups and batteries – to teach underserved families about engineering and computational thinking. "Over the next few years, artificial intelligence will change our economy and the way we work. It's vital that we train parents and their children to adopt a new mindset - one of lifelong learning," said Tara Chklovski, CEO and Founder, Iridescent. "We are excited to be working with Google – one of the leading experts on artificial intelligence – to help underserved families and communities engage with the most cutting-edge innovations."

I decided to develop familiarity with computer vision and machine learning techniques. As a web developer, I found this growing sphere exciting, but did not have any contextual experience working with these technologies. I am embarking on a two year journey to explore this field. If you haven't read it already, you can see Part 1 here: From webdev to computer vision and geo. I ended up getting myself moving by exploring any opportunity I had to excite myself with learning.

Plans to use algorithms to identify failing schools have been criticised by the National Association of Head Teachers. A data science unit, part-owned by the UK government, has been training algorithms to rate schools, using machine learning - a form of AI. It plans to work with England education watchdog Ofsted to help prioritise inspections. The NAHT said effective inspection of schools should not be based on data. "We need to move away from a data-led approach to school inspection," the union said in a statement.

About this course: The value of IoT can be found within the analysis of data gathered from the system under observation, where insights gained can have direct impact on business and operational transformation. Through analysis data correlation, patterns, trends, and other insight are discovered. Insight leads to better communication between stakeholders, or actionable insights, which can be used to raise alerts or send commands, back to IoT devices. With a focus on the topic of Exploratory Data Analysis, the course provides an in-depth look at mathematical foundations of basic statistical measures, and how they can be used in conjunction with advanced charting libraries to make use of the world's best pattern recognition system – the human brain. Learn how to work with the data, and depict it in ways that support visual inspections, and derive to inferences about the data.

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate "worldviews" on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of "we're all equal" (WAE) and "what you see is what you get" (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (college admissions; credit application; insurance contracts) and map out the policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence.

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Iliya is a Finance graduate with a strong quantitative background who co-founded 365 Data Science, an educational start-up focused on data science. Since then, he has been creating online courses focused on statistics and machine learning.

Researchers, entrepreneurs and tech giants are taking artificial intelligence (AI) to new frontiers and transforming daily activities in health, education and commerce.

First, the disclaimer, I am not an IT engineer:-) I work in the field of semiconductors, specifically high-power semiconductors, as a technology development engineer, whose day job consists of dealing primarily with semiconductor physics, finite-element simulation of silicon fabrication process, or electronic circuit theory. There are, of course, some mathematics in this endeavor, but for better of worse, I don't need to dabble in the kind of mathematics that will be necessary for a data scientist. However, I have many friends in IT industry and observed a great many traditional IT engineers enthusiastic about learning/contributing to the exciting field of data science and machine learning/artificial intelligence. I am dabbling myself in this field to learn some tricks of the trade which I can apply to the domain of semiconductor device or process design. But when I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don't know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics. Now, I have a Ph.D. in Electrical Engineering from a reputed US University and still I felt incomplete in my preparation for having solid grasp over machine learning or data science techniques without having a refresher in some essential mathematics.

The security operations center (SOC) at the University of Texas A&M System serves 11 universities and seven state agencies. But with just seven full-time analysts and a risk-rich environment of 174,000 students and faculty, triaging security events was overwhelming. Security analysts had to look at network flow traffic and logs from disparate systems to determine which security events posed threats that needed investigating. The division of labor was typical: Tier-1 analysts looked at alerts, Tier-2 analysts hunted down likely attacks, and a security engineer dreamed up better ways to make the infrastructure more secure. And even the most knowledgeable analysts took a long time to connect disparate data points to come up with a threat profile.