The Data Incubator recently teamed up with MRINetwork to increase its access to hiring partnerships worldwide. MRINetwork is comprised of over 1,500 search professionals who specialize in hundreds of industries, many of whom came from the industries in which they now recruit. The addition of MRINetwork, and its network of existing clients, will add thousands of hiring partners on top of TDI's existing 300 hiring partnerships. As the need for data scientists has increased exponentially over the past few years, MRI provides TDI students with immediate access to new data science positions in geographies worldwide, as well as greater access to companies with a fundamental need for the data science talent required to harness the power of their data.

Six titans of industry stood onstage at MIT's Kresge Auditorium yesterday, assembled to speak on a panel about artificial intelligence (AI), including David H. Koch Institute professor Robert Langer; Helen Greiner, cofounder of iRobot, the Bedford-based company perhaps best known for its line of autonomous vacuum cleaners; Xiao'ou Tang, founder of computer vision startup SenseTime, which last year raised $1.2 billion in venture capital at a valuation of more than $4.5 billion; and Eric Schmidt, former executive chairman of Google. The discussion capped off a three-day celebration of MIT's new Stephen A. Schwarzman College of Computing, which will offer its first classes in physics, economics, biology, economics, machine learning, and related disciplines this fall. The panelists shared thoughts on a range of topics, but one they repeatedly touched on was entrepreneurship. Entrepreneurs, Schmidt argued in his opening remarks, drive the economy -- they're spigots for ideas that form the basis of industries. "[Founders are] people who are filled with a vision -- something they care about -- and they personalize it, they believe in it, and they convince others to follow them," he said. But, he said, they're in "need [of] more juice."

There are loads of Data Science courses at Udemy, not just the ones listed above. If none of these take your fancy, have a look around and I'm sure you'll find others that might just hit the spot. I also recommend taking a look at courses in Statistics, Artificial Intelligence, Machine Learning and Deep Learning too. Udemy's list changes every 30 days, so I will update this post regularly to reflect these changes. Final word - when you've done any of these courses, please return and leave some feedback and a review in the comments below.

AI For Everyone will launch on February 28th! This non-technical course will teach you the language of AI, how to plan and execute successful AI projects, and how to drive AI adoption in your company. This course is taught by deeplearning.ai

Mark Cahill, managing director for the ManpowerGroup, UK, commented that companies were deploying a myriad of approaches to upskill their existing workforce and build talent further, with many employers turning to long-term training courses. Online learning management systems are a popular channel for organizations to use, providing mass content which is especially useful for onboarding, compliance and cybersecurity training. Companies need to promote a culture of learning, provide career guidance, and offer short, focused upskilling opportunities. People need to know how to prepare for high growth roles of the future and that their employer supports their learning. As well as providing internal in-person and online training, companies can tap into external resources by partnering with organizations such as schools, universities and industry bodies to build communities of talent." The report also found that demand for IT skills is growing significantly: 16% of employers expect to increase headcount in IT, five times more than those expecting a decrease. The vast majority of employers in the U.S plan to increase or maintain headcount as a result of automation. Upskilling is on the rise, with 76% of companies planning to upskill their workforce by 2020, up from 28% in 2011. In the UK, 95% of employers are planning to increase or maintain headcount as a result of automation, according to the report. The research found that companies that are digitalizing are growing and this growth is producing more and new kinds of jobs. Cahill argued that the narrative around automation and AI "stealing our jobs" couldn't be further from the truth. As robots enter the workforce, they are transforming jobs but equally creating more employment opportunities as well. Every industry needs to accept this revolution is here to stay. Employers need to work out how to manage the shift and get humans to collaborate with machines."

We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design of such adaptive algorithms is to calibrate a so-called step-size or learning rate hyperparameter depending on variance, gradient norms, etc. A recent technique promises to overcome this difficulty by maintaining multiple learning rates in parallel. This technique has been applied in the MetaGrad algorithm for online convex optimization and the Squint algorithm for prediction with expert advice. However, in both cases the user still has to provide in advance a Lipschitz hyperparameter that bounds the norm of the gradients. Although this hyperparameter is typically not available in advance, tuning it correctly is crucial: if it is set too small, the methods may fail completely; but if it is taken too large, performance deteriorates significantly. In the present work we remove this Lipschitz hyperparameter by designing new versions of MetaGrad and Squint that adapt to its optimal value automatically. We achieve this by dynamically updating the set of active learning rates. For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.

We are interested in a framework of online learning with kernels for low-dimensional but large-scale and potentially adversarial datasets. Considering the Gaussian kernel, we study the computational and theoretical performance of online variations of kernel Ridge regression. The resulting algorithm is based on approximations of the Gaussian kernel through Taylor expansion. It achieves for $d$-dimensional inputs a (close to) optimal regret of order $O((\log n)^{d+1})$ with per-round time complexity and space complexity $O((\log n)^{2d})$. This makes the algorithm a suitable choice as soon as $n \gg e^d$ which is likely to happen in a scenario with small dimensional and large-scale dataset.

Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). Popular example of scoring rule for continuous outcomes is the continuous ranked probability score (CRPS). We consider the case where several competing methods produce online predictions in the form of probability distribution functions. In this paper, the problem of combining probabilistic forecasts is considered in the prediction with expert advice framework. We show that CRPS is a mixable loss function and then the time independent upper bound for the regret of the Vovk's aggregating algorithm using CRPS as a loss function can be obtained. We present the results of numerical experiments illustrating the proposed methods.

Twenty years from now we will speak all languages, recognise all faces, remember conversations and diseases that kill people today but will be detected much earlier now, thanks to Artificial Intelligence (AI) powered systems. In 50 years, it might be possible children born then will live to at least 200 years; and climate change will come to a halt! The world will be completely powered by alternate sources of energy instead of burning fossil fuels. In fact, Thrun, 51, who co-founded and runs three startups simultaneously, is working towards some of these goals himself. Udacity is for online learning, offering nano-degrees (short courses) in areas including drones and machine learning; Kitty Hawk Corp is making electric planes and flying cars while AI powered Cresta.ai is trying to automate repetitive jobs.

We show how to take any two parameter-free online learning algorithms with different regret guarantees and obtain a single algorithm whose regret is the minimum of the two base algorithms. Our method is embarrassingly simple: just add the iterates. This trick can generate efficient algorithms that adapt to many norms simultaneously, as well as providing diagonal-style algorithms that still maintain dimension-free guarantees. We then proceed to show how a variant on this idea yields a black-box procedure for generating optimistic online learning algorithms. This yields the first optimistic regret guarantees in the unconstrained setting and generically increases adaptivity. Further, our optimistic algorithms are guaranteed to do no worse than their non-optimistic counterparts regardless of the quality of the optimistic estimates provided to the algorithm.