Ngee Ann Polytechnic and Centre for Finance, Technology and Entrepreneurship (CFTE) are about to launch AI in Finance (AIF), the first online programme for finance professionals. Despite the growing hype around Artificial Intelligence (AI), many finance professionals are still unfamiliar with the impact it will have on their industry. Ngee Ann Polytechnic (NP), one of Singapore's leading institutes of higher learning, is partnering with London-based Centre for Finance, Technology and Entrepreneurship (CFTE) to launch the first online course to showcase AI applications and use cases in the banking industry. "AI is a technological driving force that no industry can ignore. Some studies estimate that about 50 per cent of today's tasks would be assisted by AI in the next 20 years. With Singapore and London gaining recognition as leading fintech hubs of the world, it is timely for NP and CFTE to launch an industry-led course that provides finance professionals and others a practical guide to AI." "You will also see that a lot more can be automated in future. If you want to keep your job, you need to question both what your role will be in this automated future, and what impact artificial intelligence will have on your area of the business."

A study by Stanford University indicates that virtual reality, adaptive learning or analytical learning will be common in the classroom within fifteen years. Although Artificial Intelligence (AI) is already part of our lives, it is still strange to hear about it in areas such as education, where the reality of the classroom advances at a much slower pace than that of technology. However, it is precisely the educational field that could be reinforced and transformed the most thanks to the new artificial intelligence systems and their capacity to contribute to the personalisation of learning. This is what a group of researchers and academics believe that, backed by Standford University, published last September the report Artificial Intelligence and Life in 2030. According to the study, virtual reality, adaptive learning, analytical learning and online teaching will be common in classrooms in just fifteen years.

A study by Stanford University indicates that virtual reality, adaptive learning or analytical learning will be common in the classroom within fifteen years. Although Artificial Intelligence (AI) is already part of our lives, it is still strange to hear about it in areas such as education, where the reality of the classroom advances at a much slower pace than that of technology. However, it is precisely the educational field that could be reinforced and transformed the most thanks to the new artificial intelligence systems and their capacity to contribute to the personalisation of learning. This is what a group of researchers and academics believe that, backed by Standford University, published last September the report Artificial Intelligence and Life in 2030. According to the study, virtual reality, adaptive learning, analytical learning and online teaching will be common in classrooms in just fifteen years.

TL;DR - You can take the ML course on Coursera and you're magically a data scientist, because three really intelligent people did it. I'm not claiming the people referenced in this article are not data scientists who score high in Kaggle competitions. They're probably really intelligent people who picked up a new skill and excelled at it (although one was already an actuary, so he is basically doing machine learning in some form already). Here is my problem with it - being a data scientist usually requires a much larger skill set than a basic understanding of a few learning algorithms. I'm taking the Coursera ML course right now, and I think it is great!

Udacity's School of Artificial Intelligence has officially opened our new Deep Reinforcement Learning Nanodegree program for enrollment, and in doing so, we have completed a whirlwind effort that began at Intersect back in March of this year, when our School of AI was officially unveiled to the world: Today, anyone interested in entering the incredible world of Artificial Intelligence has the opportunity to do so, through the learning portal that is our School of AI. Upon arrival to the school's home page, you are prompted by a simple question: It's actually not that simple a question, of course, but we strive to make it so by offering you clear paths to pursue, depending on your current skills and experience, and your ultimate objectives. Whether you're new to the field, or already a working professional, we offer you a point-of-entry. Whether you want to work at a company focused on AI, or bring new AI techniques to a company that can benefit from them, we offer tailored curriculum to support your journey. Perhaps you're simply a future-minded thinker who sees where the world is headed, and you want to start planning ahead by adding valuable skills to your toolkit now.

While I did learn a lot of maths while doing my engineering degree, I forgot most of it by the time I wanted to get into Machine Learning. After I graduated I never really had a need for any of the maths. I did a lot of web programming which relied on logic and I can honestly say that with each system with the word'Management' in the title I lost a third of my math knowledge! I've programmed extensions for Learning Management Systems, Content Management Systems and Customer Relationship Management Systems -- I'll leave you to figure out how much math apptitude I had after working with these systems. At the moment I've got good data science skills and can use a variety of ML and DL algorithms.

Some 70 percent of companies claim they're using a form of artificial intelligence (A.I.), according to a new report by Constellation Research. That includes machine learning, deep learning, natural language processing, and cognitive computing. But while companies are interested in what A.I. can potentially do for them, many aren't willing to invest massive amounts of money in the endeavor. Some 92 percent of respondents reported overall A.I. budgets of less than $5 million, with 52 percent paying less than $1 million. However, most plan to increase their A.I.-related spending over the next year.

Jeff Maggioncalda was recently named CEO of Coursera. I have interviewed both founders of the company, Andrew Ng and Daphne Koller, so I was curious about Maggioncalda's perspective on the company, education technology and the massive open online courses more generally, and his own background as an entrepreneur. Regarding the last point, Maggioncalda was previously the CEO of Financial Engines Inc, a company co-founded by economist and Nobel Prize winner William Sharpe and recently sold for $3 billion. During his 18 years as CEO of Financial Engines Inc, Maggioncalda had to pivot three times from his original idea before becoming a success. Financial Engines would go on to beocme the largest independent online retirement advice platform with more than $100b under management.

Are you looking for game design and development courses? Here is the list of best game development courses, tutorials, training and certification for the individuals interested in becoming a game developer, game designer, game artist or a game programmer. Do you want to learn how to develop games? Then these Game Development Online Courses will show you the right path to get started. Building games is an innovative and technical art form.

We study the problem of online learning to re-rank, where users provide feedback to improve the quality of displayed lists. Learning to rank has been traditionally studied in two settings. In the offline setting, rankers are typically learned from relevance labels of judges. These approaches have become the industry standard. However, they lack exploration, and thus are limited by the information content of offline data. In the online setting, an algorithm can propose a list and learn from the feedback on it in a sequential fashion. Bandit algorithms developed for this setting actively experiment, and in this way overcome the biases of offline data. But they also tend to ignore offline data, which results in a high initial cost of exploration. We propose BubbleRank, a bandit algorithm for re-ranking that combines the strengths of both settings. The algorithm starts with an initial base list and improves it gradually by swapping higher-ranked less attractive items for lower-ranked more attractive items. We prove an upper bound on the n-step regret of BubbleRank that degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive numerical experiments on a large real-world click dataset.