Editor's note: Kevin Gray is president of Cannon Gray LLC, a marketing science and analytics consultancy. He would like to thank Marco Vriens of Ipsos for his helpful comments on a draft of this article. Machine learning gets a lot of buzz these days, usually in connection with big data and artificial intelligence (AI). But what exactly is it? Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering.

Semidefinite programming has become a key optimization tool in many areas of applied mathematics, signal processing and machine learning. SDPs often arise naturally from the problem structure, or are derived as surrogate optimizations that are relaxations of difficult combinatorial problems [7, 1, 8]. In spite of the importance of SDPs in principle--promising efficient algorithms with polynomial runtime guarantees--it is widely recognized that current optimization algorithms based on interior point methods can handle only relatively small problems. Thus, a considerable gap exists between the theory and applicability of SDP formulations. Scalable algorithms for semidefinite programming, and closely related families of nonconvex programs more generally, are greatly needed. A parallel development is the surprising effectiveness of simple classical procedures such as gradient descent for large scale problems, as explored in the recent machine learning literature. In many areas of machine learning and signal processing such as classification, deep learning, and phase retrieval, gradient descent methods, in particular first order stochastic optimization, have led to remarkably efficient algorithms that can attack very large scale problems [3, 2, 10, 6]. In this paper we build on this work to develop first-order algorithms for solving the rank minimization problem under random measurements and a closely related family of semidefinite programs. Our algorithms are efficient and scalable, and we prove that they attain linear convergence to the global optimum under natural assumptions.

We all have memories of high school. Most of them, like the time spent with our friends, are great memories that we will always cherish. However, other memories, like the stress of taking the SAT's, are ones we try not to think about. In fact, just thinking about it now makes me sweat. Being a teenager is stressful enough, but the idea that so much of your future depends on this one test is beyond nerve wracking. That is why so many parents try to help by paying for their kids to take test prep courses, so they will walk in on test day feeling prepared.

Hi all - I'm seeking advice on how to best learn the math required for doing machine learning research, particularly with regard to neural nets (and other graphical models - sorry if I'm using these terms incorrectly). My background is in cognitive science, but of a particularly computational flavor, so I've been exposed to the high level ideas behind "connectionist" models, and have used them as a sort of black box in the context of comparing their performance to human behavioral data. But my undergrad coursework is conspicuously lacking in math. I recently got a job as a software engineer in a lab that works on deep learning (in NLP applications), and I want to be able to understand the math well enough to contribute to research. The lab PI and I have discussed my interest in eventually converting to a grad student, so I want to make sure my math abilities are solid as soon as I can.

Associate Professor Julian Togelius works at the intersection of artificial intelligence (AI) and games--a largely unexplored juncture that he has shown can be the site of visionary and mind-expanding research. Could games provide a better AI test bed than robots, which--despite the way they excite public imagination--can be slow, unwieldy and expensive? According to him, the answer is resoundingly yes. "I'm teaching computers to be more creative than humans," he says. Togelius, a member of the NYU Tandon School of Engineering's Department of Computer Science and Engineering, is at the forefront of the study of procedural content generation (PCG)--the process of creating game content (such as levels, maps, rules, and environments) by employing algorithms, rather than direct user input.

Data Skeptic takes a different take on how we review data--thanks to some healthy skepticism, listeners come out with unusual information and knowledge. The show alternates between interviews with industry experts, and mini episodes wherein the host explains data science tidbits to his non data scientist wife. The tone of this show is simultaneously intellectual and a bit off beat. It's fun, and easier to follow than highly technical podcasts. If you need a series with nice production quality and clear, friendly radio voices, this may be the one.

A small step for Google may very soon become a giant step for mankind. An artificially intelligent computer system built by Google has just beaten the world's best human, Lee Sedol of South Korea, at an ancient strategy game called Go. Go originated in Asia about 2,500 years ago and is considered many, many times more complex than chess, which fell to AI back in 1997. Google's programmers didn't explicitly teach AlphaGo – that's what the system is called - to play the game. Instead, they built a sort of model brain called a neural network that learned how to play Go by itself. As it studied a database of about 100,000 human matches, and then continued by playing against itself millions of times, it constantly reprogrammed itself and improved.

Machine learning is already a very big deal. It's here, and it's in use in far more businesses than you might suspect. A few months back, I decided to take a deep dive into this topic to learn more about it. In today's post, I'll dive into a certain amount of technical detail about how it works, but I also plan to discuss its practical impact on SEO and digital marketing. For reference, check out Rand Fishkin's presentation about how we've entered into a two-algorithm world.

I will start by giving some background information. Currently I am a final year (graduation year) CS student who got interested in machine learning about 6 months ago. I started with the Andrew NG course from Coursera which I recently finished (about 3 weeks ago). When I finished the Coursera course I saw a suggestion that if you'd like to continue to learn more about machine learning you could follow the online Coursera specialization from the Washington university. In this AMA he suggested that if you'd like to learn more about machine learning one of the things you could do was to follow and complete the Coursera course from Andrew NG and their specialization course.

The Unit for Natural Language Processing [1] of the Insight Centre for Data Analytics [2] at the National University of Ireland, Galway [3], invites applications for two PhD positions in Natural Language Processing and Linked Data. The positions are associated with the SFI funded research program on Text Mining with Linked Data. Candidates should preferably have a Masters degree in a relevant field of study with an emphasis on areas such as text mining, natural language processing, computational linguistics, machine learning etc. Please send your application, including CV and a research proposal of up to two pages (both in PDF only) before the closing date of April 25th, 2016 to Dr. Paul Buitelaar at paul.buitelaar@insight-centre.org