I have tried to exhibit credit scoring case studies with German Credit Data. This article includes detail programming of predictive modeling 1. Univariate And Bi-Variate Analysis 2. Information Value and Weight Evidence to access prediction power of variables 3. Multivariate Analysis and Dimension Reduction using Variable Clustering 4. Different Machine Learning Techniques and their performance evaluation using ROC, AUC and KS The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to setup a modelimg framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns". Hence the first one requires analyst to have solid understanding of statistical techniques and business knowledge while the later one is more complex in nature and computational intensive, hence requires higher computation power of the systems and analyst needs to be tech savvy. Kindly note that while traditional techniques perform well on small to large amount of data, machine learning will certainly learn better on high-dimensional and complex data such as BigData setup. If you want to do more experiments and not sure where to get a problem definition or data to machine learning, you may explore the online machine learning repository here http://archive.ics.uci.edu/ml/.

If you start feeling a cold or flu coming on this season, you will be able to reach out to IBM's artificial intelligence-fueled Watson to find some answers to your sniffly, coughy questions. GlaxoSmithKline, (GSK) the world's sixth-largest pharmaceutical company, is teaming up with IBM to use Watson to better connect with customers. The London-based company plans to start using Watson Ads in November, enabling people to ask questions by voice or text right through GSK's online ads. Jason Andree, senior brand manager of the Cough and Cold division of GlaxoSmithKline North America, will announce the move at the start of the IBM World of Watson conference in Las Vegas this week. The event, which focuses on changing business through cognitive computing, analytics and big data, runs from today to Thursday.

Almost half of UK grocery retail directors say replenishment is still driven by gut feel, according to research by Blue Yonder, which supplies predictive applications of retail. It said that interviews with 750 grocery managers and directors in the USA, UK, Germany and France, showed that despite a rise in accurate machine learning algorithms for automated replenishment and demand planning, 46 per cent of surveyed directors in the UK say replenishment is still an entirely manual process. Some 85 per cent of respondents identified automation as a key tool for making the fast decisions needed to meet customer demand. The research also identified that 31 per cent of directors in the UK feel there are now too many decisions to be made manually, with the same number stating that gut feel is slowing them down. The research also found that 62 per cent of UK directors say they have invested in replenishment optimisation in the last two years; 31 per cent say they will be investing further in replenishment optimisation in the next two years.

The dream of thinking machines goes back centuries, at least to Gottfried Wilhelm Leibniz, in the 17th century. Leibniz (right) helped invent mechanical calculators, independently of Isaac Newton developed the integral calculus, and had a lifelong fascination with reducing thinking to calculation. His Mathesis Universalis was a vision of universal science made possible by a mathematical language more precise than natural languages, like English. The Limits of Modern AI: A Story In the 18th Century the Enlightenment philosopher and proto-psychologist Étienne Bonnot de Condillac imagined a statue outwardly appearing like a man and also with what he called "the inward organization." In an example of supreme armchair speculation, Condillac imagined pouring facts--bits of knowledge--into its head, wondering when intelligence would emerge. Condillac's musings drew inspiration from the early mechanical philosophy of Thomas Hobbes, who had famously declared that thinking was nothing but ...

The small drone, with its six whirring rotors, swept past the replica of a Middle Eastern village and closed in on a mosque-like structure, its camera scanning for targets. No humans were remotely piloting the drone, which was nothing more than a machine that could be bought on Amazon. But armed with advanced artificial intelligence software, it had been transformed into a robot that could find and identify the half-dozen men carrying replicas of AK-47s around the village and pretending to be insurgents. As the drone descended slightly, a purple rectangle flickered on a video feed that was being relayed to engineers monitoring the test. The drone had locked onto a man obscured in the shadows, a display of hunting prowess that offered an eerie preview of how the Pentagon plans to transform warfare. Almost unnoticed outside defense circles, the Pentagon has put artificial intelligence at the center of its strategy to maintain the United States' position as the world's dominant military power.

Parag Mital is director of machine intelligence at Kadenze, as well as an artist and interdisciplinary researcher obsessed with the nature of information, representation and attention. Artificial intelligence allows machines to reason and interact with the world, and it's evolving at a breakneck pace. Many advances in AI can be attributed to machine learning, which works by tapping massive computing power to crunch through enormous amounts of digitized data. Now consider that most of our data, the best minds in the business and more computing power than you could ever imagine sit with just a handful of companies. For these reasons, only a few companies in the world are best situated to understand the true potential -- and the current limits -- of AI.

People who text and drive may want to think twice, as there could soon be more than the police on their case. An AI-enhanced speed camera has been designed that will catch people on their phones and notify the authorities directly. The futuristic technology could be used to monitor behaviours including leaving suspicious packages, drivers distracted by mobile devices, and intruders trying to access secure locations. People who text and drive may want to think twice, as soon there could be more than the police on their case. Advanced algorithms reside inside cameras themselves, and information processing occurs instantaneously, rather than the information needing to be sent to the cloud to be processed.

The residual plot above shows the prediction error of the test dataset plotted against a selected feature. We built this model just before the wild-card round of the NFL playoffs, and we wanted to test the model against 10 previous games. Of our 10 predictions, seven were correct, and two of the three incorrect predictions were very close to margin (50 percent), as seen in the table below. So, we were comfortable with this model. Next, our model correctly predicted the outcome of three out of four playoff games.

John Lewis [IRDX RJLW] is investing in retail tech startups working in areas from machine learning to social media following the completion of its latest JLAB accelerator programme. The retailer, an Elite trader in IRUK Top500 research, and its innovation partner L Marks will together put 100,000 into DigitalBridge, a technology company that uses computer vision and machine learning technology to enable customers to see how new home furnishings will look within their homes. Wedding Planner, which enables couples to plan their wedding over their phones and online, and Link Big, whose technology turns Instagram into a social checkout, enabling customers to buy products seamlessly from their Instagram feed shop, both receive 50,000. The John Lewis Buying teams will continue working with the two other startups on JLAB 2016, Ding Labs and Robotical, with a view of helping to bring their products to market. The five startups were part of JLAB 2016, a ten week programme working within John Lewis operations to put their technology to practical use.

Today's IT leaders are facing a world of unknowns and underlying fears on a daily basis - from the ransomware that could take down their organizations, to the emergence of new digital disruptors that could render their business obsolete, to the absence of quality IT talent they need to stay ahead of these and other threats. Although scary, it is comforting to know that you are not alone. We asked 13 IT leaders to share their stories of unexpected or frightening events in their career, or the threats on the horizon making them nervous for the future of IT. "A Fortune 500 company had an estimated 500,000 boxes of business records stored with both physical records management vendors and at the company's locations throughout the United States. They pay 2 million per year in box storage alone, but they have no idea what is in the hardcopy business records; the boxes are the'unindexed unknowns.' There is likely a significant risk for PII and PCI given the industry the organization is in. Presently, the company does not have the internal bandwidth nor financial appetite to locate, index, and digitize all of the paper records, thus leaving them extremely vulnerable to a breach."