Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within'quant' teams. 'Quants' (development analysts) design and implement complex models and are sought after by banks, fund managers, insurance companies, hedge funds, and financial software and data providers. Programming experience is an advantage but is not mandatory. Relevant work experience is also taken into account. The programme is delivered through a combination of lectures, tutorials, seminars, and project work.

Get the Intel Math Kernel Library: http://intel.ly/2hu1qQb

Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects - swarm intelligence - resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. The study of ant colonies behavior and of their self-organizing capabilities is of interest to knowledge retrieval/ management and decision support systems sciences, because it provides models of distributed adaptive organization which are useful to solve difficult optimization, classification, and distributed control problems, among others. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems.

Since the earliest days of the Internet, most government agencies have eagerly explored how to use technology to better deliver services to citizens, businesses and other public-sector organizations. Early on, observers recognized that these efforts often varied widely in their implementation, and so researchers developed various frameworks to describe the different stages of growth and development of e-government. While each model is different, they all identify the same general progression from the informational, for example websites that make government facts available online, to the interactive, such as two-way communication between government officials and users, to the transactional, like applications that allow users to access government services completely online. However, we will soon see a new stage of e-government: the perceptive. The defining feature of the perceptive stage will be that the work involved in interacting with government will be significantly reduced and automated for all parties involved.

Large platform companies like Amazon, Apple, Google, Samsung, and Microsoft want to provide the operating system for our lives, and they will fight hard in 2017 to establish their foothold in the emerging technologies we will likely come to rely on in the future. Those with the most complete product offerings have an advantage. Since people like to buy products that play well with the other products they already own, a platform company risks losing customers by not having a product in a hot category. These large companies already have an advantage over smaller companies due to their massive R&D budgets and their ability to hire the best people to build the stuff we want now and to anticipate the technology we'll want in the future. And if a hot product is developed by some ambitious startup, these giants can easily swoop in and acquire both the product and the people who created it.

We have seen other great developments such as with image recognition, where we can one day expect to see computers that will be able to read X-ray, MRI and CT scans more efficiently than radiologists, enabling the quicker diagnosis of cancer. This is just one example of how the progress of deep learning is rapidly advancing and impacting the world we live in, from the way we shop to predicting energy sources to shaping modes of transport. We asked some of our influential speakers, who will be presenting at our deep learning summits this year, for their predictions for deep learning in 2017. In 2017, we will probably see further rapid exploration of applications of current deep learning techniques, as well as further theoretical advances, improving robustness and sample efficiency. We will also see various fun new applications of deep learning to image and voice resynthesis.

Last week IBM focused attention on "five technologies that [they] believe have the potential to change the way people work, live, and interact during the next five years." They call their vision "5 in 5". The technologies they chose all have to do with enhancing our ability to visualize the world from the micro to the macro level. Here's what IBM sees in our future. Mental and physical disorders with a neurophysiological basis such as Alzheimer's and Parkinson's disease can affect the language processing areas in the brain.

Neural networks have the rather uncanny knack for turning meaning into numbers. Data flows from the input to the output, getting pushed through a series of transformations which process the data into increasingly abstruse vectors of representations. These numbers, the activations of the network, carry useful information from one layer of the network to the next, and are believed to represent the data at different layers of abstraction. But the vectors themselves have thus far defied interpretation. In this blog post I put forward a possible interpretation of these vectors. I argue we shouldn't take these vectors literally, but rather as an encoding for a simpler, sparse data structure.

Last week I went to the workshops at NIPS (biggest ML conference in the world) and I also attended part of the ML and the Law symposium the day before. I found out a little bit too late about the symposia but I was still able to attend two panels on which there were both lawyers and computer scientists. They were very insightful and informative -- did you know that this Spring, the European Union passed a regulation giving its citizens a "right to an explanation" for decisions made by machine-learning systems? The panel discussions were motivated by the problem of explaining ML-powered decisions which have an important impact on people's lives: We need to be able to test how systems get to their conclusions; if we can't test, we can't contest. Individuals are entitled to know which data is being processed of them, and to explanations of how predictions & decisions work, in terms they can understand.

Artificial Intelligence is becoming more and more prevalent in every day life as we see the technology adopted in everything from digital assistants to autonomous vehicles. One sector that has huge potential for AI is retail. You might not know it, but if you've ever submitted an online query to a retailer, then you've probably already spoken to an AI. Brands and companies are quickly beginning to realise the benefits of automation, applying AI not only to behind-the-scenes operations but also to customer services. This is causing huge changes to the way that retail companies work, from tourism to banking. With AI startups now offering adaptable software, it's easier than ever for businesses to integrate the tech into their business strategies.