Another buzzer sounds, drivers pick up their controls and all six robots--three per alliance--are now under human control. As these huge 120-pound robots score points, cheers ring through a packed stadium, fueled by high school students who worked hard to build their robot in just six weeks. As the match ends, nervous and excited students wait to see who is the winner of the 2016 world championship. This was my last match as a member of the Girls of Steel FIRST Robotics Competition Team #3504. FIRST (For Recognition and Inspiration of Science and Technology) is a robotics program for students from K-12, and I was in the last division, FRC.

Data scientists are expected to be familiar with the differences between supervised machine learning and unsupervised machine learning -- as well as ensemble modeling, which uses a combination of techniques, and semi-supervised learning, which combines supervised and unsupervised approaches. While it's not necessarily new, deep learning has recently seen a surge in popularity as a way to accelerate the solution of certain types of difficult computer problems, most notably in the computer vision and natural language processing (NLP) fields. By extracting high-level, complex abstractions as data representations through a hierarchical learning process, deep learning models yield results more quickly than standard machine learning approaches. Machine learning, deep learning, and artificial intelligence all have relatively specific meanings, but are often broadly used to refer to any sort of modern, big-data related processing approach.

Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won't always have access to the optimal answer, or maybe there isn't an optimal correct answer. You'd want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?

This course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. We also expose you to three software packages so you can develop your coding skills by completing practical exercises. This course is part of the Big Data Analytics program with FutureLearn, which will enable you to gain the big data analytics skills that are in demand today.

We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.

This paper focuses on convex constrained optimization problems, where the solution is subject to a convex inequality constraint. In particular, we aim at challenging problems for which both projection into the constrained domain and a linear optimization under the inequality constraint are time-consuming, which render both projected gradient methods and conditional gradient methods (a.k.a. the Frank-Wolfe algorithm) expensive. In this paper, we develop projection reduced optimization algorithms for both smooth and non-smooth optimization with improved convergence rates under a certain regularity condition of the constraint function. We first present a general theory of optimization with only one projection. Its application to smooth optimization with only one projection yields $O(1/\epsilon)$ iteration complexity, which improves over the $O(1/\epsilon^2)$ iteration complexity established before for non-smooth optimization and can be further reduced under strong convexity. Then we introduce a local error bound condition and develop faster algorithms for non-strongly convex optimization at the price of a logarithmic number of projections. In particular, we achieve an iteration complexity of $\widetilde O(1/\epsilon^{2(1-\theta)})$ for non-smooth optimization and $\widetilde O(1/\epsilon^{1-\theta})$ for smooth optimization, where $\theta\in(0,1]$ appearing the local error bound condition characterizes the functional local growth rate around the optimal solutions. Novel applications in solving the constrained $\ell_1$ minimization problem and a positive semi-definite constrained distance metric learning problem demonstrate that the proposed algorithms achieve significant speed-up compared with previous algorithms.

Best price ends June 23. We've been talking about data science and data scientists for a decade now. While there's always been some debate over what "data scientist" means, we've reached the point where many universities, online academies, and bootcamps offer data science programs: master's degrees, certifications, you name it. The world was a simpler place when we only had statistics. But simplicity isn't always healthy, and the diversity of data science programs demonstrates nothing if not the demand for data scientists.

Kevin Ashley is a Professor of Law and Intelligent Systems at the University of Pittsburgh, Senior Scientist, Learning Research and Development Center, and Adjunct Professor of Computer Science. He received a B.A. from Princeton University, New Jersey, a J.D. from Harvard Law School, Massachusetts and a Ph.D. in computer science from the University of Massachusetts. A visiting scientist at the IBM Thomas J. Watson Research Center, New York, NSF Presidential Young Investigator and Fellow of the American Association for Artificial Intelligence, he is co-Editor-in-Chief of Artificial Intelligence and Law and teaches in the University of Bologna Erasmus Mundus doctoral program in Law, Science and Technology.

An AI machine has completed the maths section of the annual Chinese university exams in record time. The system, called AI-Maths, completed the test 12 times faster than it normally takes to complete. Despite the speedy result, its scores were well below average compared to students. A machine learning system called AI-Maths has completed the maths section of the annual Chinese university exams in blistering time. An AI system developed in 2014 using big data, artificial intelligence and natural language recognition technologies, had mixed results after attempting China's annual university entrance exam.

One of my very good friends, who hadn't been in a relationship for the longest time has been celebrating his six month relationship anniversary all of this month. And he's gone all out, from proposing, to travel around the country and a major party with friends and family. After the said party, I was definitely pumped up to celebrate things in my life that have lasted beyond the first day despite all odds… So I got home and partitioned my notebook into four parts then in between beer breaks took time to fill in the four slots as a way to celebrate consistency. The last two quadrants were actually really hard to fill and are still a work in progress. The first thing I wrote down was Nairobi WiMLDS which will have lasted six months, this month!! Nairobi WiMLDS, is a community of women (and men) with interests or working in data science.