A graduate student has been charged with murder in the fatal stabbing of beloved USC neuroscience professor, Bosco Tjan on campus Friday. David Jonathan Brown, 28, of Los Angeles is expected to be arraigned Tuesday in downtown Los Angeles, according to the L.A. County district attorney's office. If he is convicted, Brown faces up to 26 years to life in prison. Prosecutors allege that Brown used a knife when he attacked and stabbed Tjan in the chest at 4:30 p.m. Friday in his office in the Seeley G. Mudd Building on campus. Brown was immediately taken into custody.

In first part we explored the statistical model underlying the machine learning problem, and used it to formalize the problem in terms of obtaining the minimum generalization error. By noting that we cannot directly evaluate the generalization error of an ML model, we continued in the second part by establishing a theory that relates this elusive generalization error to another error metric that we can actually evaluate, which is the empirical error. That is: the generalization error (or the risk) $R(h)$ is bounded by the empirical risk (or the training error) plus a term that is proportionate to the complexity (or the richness) of the hypothesis space $ \mathcal{H} $, the dataset size $N$, and the degree of certainty $1 - \delta$ about the bound. Starting from this part, and based on this simplified theoretical result, we'll begin to draw some practical concepts for the process of solving the ML problem. We'll start by trying to get more intuition about why a more complex hypothesis space is bad.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Scala: Scala is a general purpose programming language - like Java or C . It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback.

This week we are celebrating Computer Science Education Week around the globe. In this "age of acceleration," in which advances in technology and the globalization of business are transforming entire industries and society itself, it's more critical than ever for everyone to be digitally literate, especially our kids. This is particularly true for women and girls who, while representing roughly 50 percent of the world's population, account for less than 20 percent of computer science graduates in 34 OECD countries, according to this report. This has far-reaching societal and economic consequences. By 2020, the U.S. Bureau of Labor Statistics predicts that there will be 1.4 million computing jobs but just 400,000 computer science students with the skills to apply for those jobs. Computer science is a top-paying college degree and computer programming jobs are growing at a rate that is double the national average, according to a National Association of Colleges and Employers report.

While Chukwuagoziem Uzoegwu was growing up, his mother often would throw what he and his brothers called "educational tantrums." On those weekends or on random days in the long stretch of summer vacation, the Uzoegwu boys would be barred from TV "from sun up to sunset," he said. "Leisure time was spent reading. Leisure time was spent writing," said Uzoegwu, now 17 and a senior at King Drew Medical Magnet High School of Medicine and Science. Uzoegwu attributes his upbringing with his success as a student.

Leading tech companies ship AI free within their products (Siri, Alexa, Google Assistant), powering our phones and the rapidly growing home personal assistant market. Indeed, they are becoming increasingly good at answering our questions, making us smarter. Teaching not rote facts and figures, but instead teaching students the paths to find this knowledge on their own. Teaching students -- as we do with computers through AI -- how to learn. We are stuck with centuries old methodologies, where schools and teachers act like the gateway to knowledge, but at a time when students can access all they want by simply asking Alexa.

With demand for graduates with AI skills booming, we've released the NVIDIA Deep Learning Teaching Kit to help educators give their students hands on experience with GPU-accelerated computing. The kit -- co-developed with deep-learning pioneer Yann LeCun, and largely based on his deep learning course at New York University -- was announced Monday at the NIPS machine learning conference in Barcelona. Thanks to the rapid development of NVIDIA GPUs, training deep neural networks is more efficient than ever in terms of both time and resource cost. The result is an AI boom that has given machines the ability to perceive -- and understand -- the world around us in ways that mimic, and even surpass, our own. "Deep learning has become one of the most important computing models, and the need for graduating students with theoretical and application expertise in this area is critical," LeCun said.

In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, that is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem.

When Amazon first coined the strapline "Ask Alexa" for its virtual assistant, it couldn't have predicted the X-rated nature of some of the requests. "She" may boast an encyclopaedic knowledge, but research by consumer behaviour analysts Canvas 8 reveals that some users are more interested in a virtual hook-up than fact finding. And she's not the only target: the equally smooth voice of Microsoft's Cortana is getting customers just as hot under the collar apparently. From perma-smiling avatars in traditionally female support roles, to hyper-sexualised "fembots" pandering to male fantasies, the female form is everywhere in techno-world - attractive, servile and at your command. A little more conservative, but just as eager to please, is virtual personal assistant Amy Ingram, the brainchild of New York start-up X.ai.

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.