For anyone who wants to learn "Data Science," I want to emphasize that it's such a fluid term. I would recommend starting out by analyzing a dataset in an area that is close to your heart, whether it's real estate pricing data for your neighborhood, or educational curriculum & outcome data. For me, it just happened to be genomic and patient health data, because that was a problem space I felt comfortable playing around in. If you explore the myriad technologies out there to help you find patterns and predict things from that data you're passionate about, before you know it you'll wake up one day and realize that you're actually a data scientist! It's all a matter of learning how to use different tools and when to apply them, and this just comes with practice.

Bill Gates speaks during the Forbes' 2015 Philanthropy Summit Awards Dinner on June 3, 2015 in New York City. Many are still wary about the effects of future artificial intelligence in humanity. But Microsoft cofounder and the world's richest man Bill Gates says AI won't be a threat, instead it will be "extremely helpful" in managing human lives. The actively evolving field of artificial intelligence has revolutionized the healthcare, business and education sectors around the world. AI also continues to prove its ubiquity by making great advances in technology and robotics but many warn about the existential risks of artificial intelligence.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

With all this talk about chatbots from Facebook and Microsoft, teaching artificial intelligence to be smarter has become a central topic of the tech world. But what about what AI can teach us? Ashok Goel, a computer science professor at Georgia Tech, put that question to the test when he added "Jill Watson" – a chatbot powered by IBM's Watson technology – to his list of of teaching assistants for an online course. The chatbot was so good at answering questions that students did not notice their TA was made of silicon until after they'd turned in their finals. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

Of course, if you are an experienced Python programmer you will be able to skip this step. Even if so, I suggest keeping the very readable Python documentation handy. KDnuggets' own Zachary Lipton has pointed out that there is a lot of variation in what people consider a "data scientist." This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Is itnecessary to intimately understand kernel methods in order to efficiently create and gain insight from a support vector machine model?

This work focuses on dynamic regret of online convex optimization that compares the performance of online learning to a clairvoyant who knows the sequence of loss functions in advance and hence selects the minimizer of the loss function at each step. By assuming that the clairvoyant moves slowly (i.e., the minimizers change slowly), we present several improved variation-based upper bounds of the dynamic regret under the true and noisy gradient feedback, which are {\it optimal} in light of the presented lower bounds. The key to our analysis is to explore a regularity metric that measures the temporal changes in the clairvoyant's minimizers, to which we refer as {\it path variation}. Firstly, we present a general lower bound in terms of the path variation, and then show that under full information or gradient feedback we are able to achieve an optimal dynamic regret. Secondly, we present a lower bound with noisy gradient feedback and then show that we can achieve optimal dynamic regrets under a stochastic gradient feedback and two-point bandit feedback. Moreover, for a sequence of smooth loss functions that admit a small variation in the gradients, our dynamic regret under the two-point bandit feedback matches what is achieved with full information.

A very popular style of research in NLP and ML is the math abstraction. You cast your learning problem as some sort of objective function that you want to optimize. Or, if you're feeling Bayesian, you write down a joint likelihood that you'll either sample from or, yes, turn into an objective function that you want to optimize. The optimizer is then typically considered a black box, aside from its hyperparameters which you often must tune. This is a very attractive style of research and one that I've personally gotten a lot of leverage out of.

There are some human attributes robots could never replace - or at least that's what you might hope. But one university has brought that into question by replacing one of their teaching assistants with a machine. Student Tyson Bailey began to wonder if Jill was a computer and posted his suspicions on Piazza. 'We were taking an AI course, so I had to imagine that it was possible there might be an AI lurking around,' said Bailey, who lives in Albuquerque, New Mexico. 'Then again, I asked Dr. Goel if he was a computer in one of my first email interactions with him.

Massachusetts Institute of Technology students this week are recreating pivotal moments leading up to the Revolutionary War. Earlier this semester, 153 students, mostly sophomores, were tasked with building robots as part of their undergraduate mechanical engineering class. On Wednesday, 137 of those students will compete in the semifinals, on a course inspired by the American Revolution, to secure one of 16 open slots in the finals Thursday. They'll join 16 other students who have already qualified for the finals. The events, held at MIT's Johnson Ice Rink, will take the robots through obstacles that include a replica dock, boat, and church steeple.

On May 8, a group of Danish researchers publicly released a dataset of nearly 70,000 users of the online dating site OkCupid, including usernames, age, gender, location, what kind of relationship (or sex) they're interested in, personality traits, and answers to thousands of profiling questions used by the site. When asked whether the researchers attempted to anonymize the dataset, Aarhus University graduate student Emil O. W. Kirkegaard, who was lead on the work, replied bluntly: "No. This sentiment is repeated in the accompanying draft paper, "The OKCupid dataset: A very large public dataset of dating site users," posted to the online peer-review forums of Open Differential Psychology, an open-access online journal also run by Kirkegaard: Some may object to the ethics of gathering and releasing this data. However, all the data found in the dataset are or were already publicly available, so releasing this dataset merely presents it in a more useful form. For those concerned about ...