This article is written for anyone who is considering becoming a data scientist. That includes young people just starting their bachelor's degrees and folks in the first two or three years of their careers who want to make the switch. It's not for folks who know they are going to pursue one of the new Master's in Data Science or Ph.D. candidates. It's for folks looking for entry level jobs that are specifically on the data science career ladder. Like many high skill professions that's not to say that an advanced degree won't make it easier but there are definitely ways to enter this market with only a bachelor's degree. If you've been practicing data science for more than five or ten years you also know that the majority of us over 35 don't have specific data science degrees. We came to data science via a variety of related disciplines and gained our cred largely based on performance and experience.

This course aimed at students with beginner to intermediate skill in F#, basic understanding of the F# syntax and a light functional understanding would be beneficial. You'll also need a computer with Linux, OSX or Windows with F# installed and an internet connection. Have you wanted to understand how to'do' machine learning or implement algorithms from a textbook in a programming language, or deploy a library to Nuget? Well, this course includes sections on machine learning using a mathematical theorem known as Bayes' Theorem. We will start by creating a predictive text engine and deploy it to Nuget, while learning how to write some basic unit tests in FsUnit.

NVIDIA CEO Jen-Hsun Huang earlier this year delivered our NVIDIA DGX-1 AI supercomputer in a box to the University of California, Berkeley's Berkeley AI Research Lab (BAIR). BAIR's over two dozen faculty and more than 100 graduate students are at the cutting edge of multi-modal deep learning, human-compatible AI and connecting AI with other scientific disciplines and the humanities. "I'm delighted to deliver one of the first ones to you," Jen-Hsun told a group of researchers at BAIR celebrating the arrival of their DGX-1. The team at BAIR are working on a dazzling array of AI problems across a huge array of fields -- and they're eager to experiment with as many different approaches as possible. To do that, they need speed, explains Pieter Abbeel, an associate professor at UC Berkeley's Department of Electrical Engineering and Computer Science.

Ron Fearing's Biomimetic Millisystems Lab at UC Berkeley is famous for its stable of bite-sized bio-inspired robots, and Duncan Haldane is responsible for a whole bunch of them. He's worked on running robots, robots with wings, robots with tails, and even robots with hairs, in case that's your thing. What Haldane and the other members of the lab are especially good at is looking to some of the most talented and capable animals for inspiration in their robotic designs. One of most talented and capable (and cutest) jumping animals is a fluffy little thing called a galago, or bushbaby. They live in Africa, weigh just a few kilos, and can leap tall (nearly two meter) bushes in a single bound.

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.