This paper and summary talk broadly survey computational sustainability research. Rather than a detailed treatment of the research projects in the area, which is beyond the scope of the paper and talk, the paper includes a meta-survey, pointing to edited collections and overviews in the literature for the interested reader. Computational sustainability research has been broadly characterized by AI methods employed, sustainability areas addressed, and contributions made to (typically, human) decision-making. The paper addresses these characterizations as well, which will facilitate a deeper synthesis later, to include the potential for developing sophisticated and holistic AI decision-making and advisory agents.

I discuss here off-the-beaten-path beautiful, even spectacular results from number theory: not just about prime numbers, but also about related problems such as integers that are sum of two squares. The connection between these numbers and prime numbers will appear later in this article. A few important unsolved mathematical conjectures are presented in a unified approach, and some new research material is also introduced, especially an attempt at generalizing and unifying concepts related to data set density and limiting distributions. The approach is very applied, focusing on algorithms, simulations, and big data, to help discover fascinating results. Even though some of the most exciting topics of mathematics are discussed here (including fundamental, century-old problems still unresolved as well as brand new hypotheses), most of the article can be understood by the layman. Among other things, you will learn some new ways to estimate Pi based on non-traditional experiments, or how a conjecture for prime numbers somehow generalizes to apply to Fibonacci numbers as well.

These videos are part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002. These videos are part of an online course, Intro to Machine Learning.

I teach one of the world's most popular MOOCs (massive online open courses), "Learning How to Learn," with neuroscientist Terrence J. Sejnowski, the Francis Crick Professor at the Salk Institute for Biological Studies. The course draws on neuroscience, cognitive psychology, and education to explain how our brains absorb and process information, so we can all be better students. Since it launched on the website Coursera in August of 2014, nearly 1 million students from over 200 countries have enrolled in our class. We've had cardiologists, engineers, lawyers, linguists, 12-year-olds, and war refugees in Sudan take the course. We get emails like this one that recently arrived: "I'll keep it short. I've recently completed your MOOC and it has already changed my life in ways you cannot imagine. I just turned 29, am in the middle of a career change to computer science, and I've never been more excited to learn."

Apache Spark is a lightning-fast framework for distributed computing that combines speed, scalability, in-memory processing, and fault tolerance with sophisticated analytics – perfect for dealing with massive datasets. This eBook takes you on a tour of Spark's powerful API; helps you create your first Spark program in Scala, Java, and Python; and gives detailed examples of real-world machine learning models from recommender systems to dimensionality reduction. You'll also learn about advanced topics like working with online machine learning and model evaluation methods using Spark Streaming. This eBook is free for today only so don't miss out!

The platform is providing a holistic online solution for teachers, students, and parents. Over two decades ago, Educomp set out to change the entire education system in the country. Since then, it claims to have empowered over 30 million learners and educators across over 65,000 schools. While Educomp was continuing to overhaul the education ecosystem through its smart class programmes, the top leadership in the company realised that they needed to take education beyond the conventional classrooms. Now, instead of taking students to classrooms, they had to flip the normal course and take classrooms to students, beyond boundaries.

Are you familiar with self-driving cars? These things would not be possible without the help of Machine Learning--the study of pattern recognition and prediction within the field of computer science. This course is taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts. They will teach you, in the simplest way possible (and with major visual techniques), to put Machine Learning and Python into action. With these skills under your belt, your programming skills will take a whole new level of power.

Artificial Intelligence or AI was seen to change the field of education in the near future. Bots may be used to do tasks that usually require large workforce. Artificial intelligence can check millions of standardized tests and make learning materials in just a short time. IT can assist human instructors in online courses. Education experts supporting AI sees the following changes in the field of education, according to Venture Beat.

This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This will be an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We will introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms.

From visual recognition to speech-to- text, the technology landscape continues to transform itself and it's happening rapidly. In 2017, the adoption and application of artificial intelligence is a more than just a far reaching dream, but a reality for most technology users. Not only is it critical that we identity these trends, but also build a workforce that adapt to these changes and build the new technologies that will advance society. We kicked off in New York City, hosting more than 40 coders, developers, early adopters, and those just interested in cognitive technology. All attendees were encouraged to take a coding course on the Learning Lab to prepare them for the event.