We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix completion, robust principal component analysis or factor analysis. It is also important in machine learning: unsupervised representation learning can often be studied through matrix factorization. We use the tools of statistical mechanics - the cavity and replica methods - to analyze the achievability and computational tractability of the inference problems in the setting of Bayes-optimal inference, which amounts to assuming that the two matrices have random independent elements generated from some known distribution, and this information is available to the inference algorithm. In this setting, we compute the minimal mean-squared-error achievable in principle in any computational time, and the error that can be achieved by an efficient approximate message passing algorithm. The computation is based on the asymptotic state-evolution analysis of the algorithm. The performance that our analysis predicts, both in terms of the achieved mean-squared-error, and in terms of sample complexity, is extremely promising and motivating for a further development of the algorithm.

The IBM Watson AI XPRIZE, a Cognitive Computing Competition, was announced on the TED Stage on Feb 17, 2016. It is a 5 million competition challenging teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges. Every year leading up to TED2020, teams will go head-to-head at World of Watson, IBM's annual conference, competing for interim prizes and the opportunity to advance to the next year's competition. The three finalist teams will take the TED stage in 2020 to deliver jaw-dropping, awe-inspiring TED Talks demonstrating what they have achieved. Ideas will be evaluated by a panel of expert judges for technical validity and ultimately, the TED and XPRIZE communities will choose the winner based on the audacity of their mission and the awe-inspiring nature of the teams' TED Talks in 2020.

Machine learning has taken a significant role in many data initiatives today. Facebook, for instance, is using machine learning to offer personalized ads, whilst Google uses it to learn about its users, and other technology companies are now able to crunch data in a fraction of the time. Organizations have been looking at machine learning as something that has the most use in looking at optimizing its current markets, but this may not be the case for too much longer. Several companies are now using machine learning combined with predictive analytics to help expand into new markets and exploit opportunities as soon as they come up. We heard about this from Wolf Rendall, Data Scientist at Auction.com, at last year's Social Media & Web Analytics Innovation Summit.

"Soon, our habitat will be invaded by unreal humans. Not only they'll influence our way of living, but also intervene in our modus operandi." I'm not the only one who thinks this way. Last week I released a list of must watch movies on Machine Learning and Data Science. I've watched 8 of them till now.

There is no dearth of articles around the web comparing and contrasting data science terminology. There are all sorts of articles written by all types of people relaying their opinions to anyone who will listen. So let me set the record straight, for those wondering if this is one of those types of posts. I think that, while there may be an awful lot of opinion pieces defining and comparing these related terms, the fact is that much of this terminology is fluid, is not entirely agreed-upon, and, frankly, being exposed to other peoples' views is one of the best ways to test and refine your own. So, while one may not agree entirely (or even minimally) with my opinion on much of this terminology, there may still be something one can get out of this.

Iris Capital is a pan-European venture capital fund manager specializing in digital economy. In such a world, many information platforms are available, but there is no software tool that applies the latest in machine and deep learning. Come to us to present us the next generation software tool or platform that automatically detects the right innovative teams/companies depending on who's looking for it and how innovation is defined. Our pitching competition is aimed at international early-stage start-ups between 1 and 5 years of existence. The 5 to 10 best start-ups will be evaluated on stage by a jury made of Iris Capital investors, large corporate innovation VP, leading start-up CEOs and media agencies.

With the massive growth of big data and the value of those data, machine learning is quickly becoming a technology that every organization should tap into. It has the ability to positively affect everything from analytics to customer relationship management (CRM). Many organizations are interested in these CRM improvements--as they should be. Machine learning in CRM can allow organizations a much more intimate look at their customer than has ever been possible in the past. Here are three ways in which machine learning can improve CRM.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

What an utterly interesting time to be alive. We all remember how Deep Blue defeated Kasparov. Chess became a'solved problem' pretty soon after that historic event. And in the past week, we have witnessed an even more amazing feat: AlphaGo beating world-class Go player Lee Sedol. The game that knows more positions than there are atoms in the universe was no match for Google's DeepMind team. It's fascinating to see how a game with relatively simple rules can lead to such complex and strategic gameplay.

You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) as well as here, here, and here.