Watch this webinar, presented by Kris Hammond, Chief Scientist of Narrative Science, to learn about the different subfields of technologies that fall under the umbrella of AI such as machine learning, advanced analytics, and advanced natural language generation. Viewers will finish the webinar understanding how the different AI technologies emulate human reasoning and how they may be able to apply these technologies to their own business.

Most Machine Learning courses are given from the perspective of a researcher/academic and focus on the theory and mathematics of the machine learning models. This workshop takes the perspective of learning by working on real machine learning problems using open source tools and platforms. We'll go all the way from data preparation to the integration of predictive models in applications and their deployment in production. "Just like development where you don't need to know a thing about computability or big-O notation to write code and ship useful and reliable software, you can work machine learning problems end-to-end without a background in statistics, probability and linear algebra." The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML & Cortana Intelligence Suite, Amazon ML, BigML) for developers getting started in Machine Learning.

Second-order optimization methods such as natural gradient descent have the potential to speed up training of neural networks by correcting for the curvature of the loss function. Unfortunately, the exact natural gradient is impractical to compute for large models, and most approximations either require an expensive iterative procedure or make crude approximations to the curvature. We present Kronecker Factors for Convolution (KFC), a tractable approximation to the Fisher matrix for convolutional networks based on a structured probabilistic model for the distribution over backpropagated derivatives. Similarly to the recently proposed Kronecker-Factored Approximate Curvature (K-FAC), each block of the approximate Fisher matrix decomposes as the Kronecker product of small matrices, allowing for efficient inversion. KFC captures important curvature information while still yielding comparably efficient updates to stochastic gradient descent (SGD). We show that the updates are invariant to commonly used reparameterizations, such as centering of the activations. In our experiments, approximate natural gradient descent with KFC was able to train convolutional networks several times faster than carefully tuned SGD. Furthermore, it was able to train the networks in 10-20 times fewer iterations than SGD, suggesting its potential applicability in a distributed setting.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree. It's no surprise then that she needed some coaching, especially since feedback is so critical to student success.

Between the University of Washington, a thriving tech community, and strong research institutions, like the Allen Institute for Artificial Intelligence (AI2), many of the rapid developments in AI are playing out in Seattle. Perhaps that's why the White House has selected the Emerald City for its first public workshop on artificial intelligence. The Office of Science and Technology Policy will co-host the first of four events on artificial intelligence at the University of Washington May 24. The workshop, put on by the UW's Tech Policy Lab and School of Law, will explore issues such as policy, logistical applications, and safety, as they relate to AI. Speakers include AI2 CEO and UW Professor of Computer Science and Engineering Oren Etzioni, White House Deputy U.S. CTO Edward Felten, Microsoft Principal Researcher Kate Crawford, and other industry experts. The workshops are intended "to spur public dialogue on artificial intelligence and machine learning and identify challenges and opportunities related to this emerging technology," writes Felton in a White House blog post.

Sabrina Knight's second-grade students at a Brooklyn public school receive lessons in coding. Some school districts in the United States are attempting to expand computer science education while the Obama administration is pushing to bring the subject to every public school in the nation. On a recent Friday afternoon at a Brooklyn public school, the children of Sabrina Knight's second-grade class listened intently as she used a peanut butter and jelly sandwich to talk about algorithms. Moments later, a student volunteer walked back and forth across the room to demonstrate looping, a technical term used in the field of computer programming. "Thumbs up if you got it," Knight said, as a flurry of 7- and 8-year-old hands and thumbs shot up in the air.

The training is broken up into two modules, an introductory machine learning module and a grid search / model selection module. The slides that accompany this tutorial are available here. R users can use RStudio or the R console to execute the R scripts. Python users can use a Jupyter/IPython notebook to execute the scripts. To execute the cells in the notebook simply highlight the cell and press Shift Enter.

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

Google is holding its biggest event of the year, showing off everything that it's going to release through 2016. The general public and developers will get their first glimpse at the products and technology that's going to rule our lives – via our phone, home, car, computer and everything else. Some of what will be announced has already leaked, and others have been spoiled by Google themselves in advance of the event. Those include rumours about a new device for the home and new technology for your phone, as well as Google's update to Android Pay that now allows it to be used in the UK. But the main event is still set to be packed with surprises, including the first details of Google's own virtual reality headset and new phone technology.

Many machine learning algorithms make assumptions about your data. It is often a very good idea to prepare your data in such way to best expose the structure of the problem to the machine learning algorithms that you intend to use. In this post you will discover how to prepare your data for machine learning in Python using scikit-learn. How To Prepare Your Data For Machine Learning in Python with Scikit-Learn Photo by Vinoth Chandar, some rights reserved. You almost always need to preprocess your data.