In this post I consider three learning approaches and argue that it could be a bad idea to avoid the mathematics and theory when starting out with machine learning. There are three approaches to starting out in machine learning that I have seen practiced. One is a bottom-up approach, in which the student starts with the mathematics and theory and then puts it into practice in either a high-level programming language -- such as Matlab, Python, R or Octave -- or by coding from scratch in a 3GL like Java, C# or C . The second is the top-down approach, in which machine learning tools and/or libraries are used to shelter the student from the coding, mathematics and theory. S/he is instructed to worry about how it all works later and to instead practice working with datasets.

While being a vibrant subfield of computer science, machine learning is used for drawing models and methods from statistics, algorithms, computational complexity, control theory and artificial intelligence. It focuses on efficient algorithms for inferring good predictive models from large data sets and is natural candidate for problems arising in HFT – both trade execution & alpha generation. In quantitative finance inference of models of predictive nature using historical data is obviously not new. Some examples include the coefficient estimation for CAPM, Fama and French factors. The granularity of data arising in HFT poses special challenges for machine learning. Often data microstructure at the resolution of individual orders, executions, hidden liquidity and cancellation including lack of understanding of how such granular data relates to actionable circumstances, namely profitably buying or selling shares, optimally executing a large order, etc.

Artificial intelligence (AI) may sound like science fiction, but it is real, and becoming increasingly important to companies in every sector. The field of artificial intelligence has produced a wide variety of "cognitive technologies" that simulate human reasoning and perceptual skills, giving businesses entirely new capabilities and enabling organizations to break prevailing tradeoffs between speed, cost, and quality. Aimed at a general business audience, this course demystifies artificial intelligence, provides an overview of a wide range of cognitive technologies, and offers a framework to help you understand their business implications. Some experts have called artificial intelligence "more important than anything since the industrial revolution." That makes this course essential for professionals working in business, operations, strategy, IT, and other disciplines.

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.

Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks. Neural networks have been around for decades, just that no one used to call them deep networks back then. Now we have all sorts of different flavors of neural networks - deep belief networks (DBNs), convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and more.

Scatterings of completed buildings, sporting new plantings of drought-tolerant grasses, are already occupied; other buildings are going up quickly, including a new fire station. There's Nissan's new Silicon Valley research center, a well-financed medical device startup called Spiracur, a digital cash startup called Quisk, and a biotech startup incubator. And there is Baidu's Silicon Valley AI Lab--my destination along this dusty road crowded with construction vehicles. It's good to spend time in a new research lab; there's not only fresh paint and hip decor--like living walls of plants--there are fresh, excited faces, and empty desks waiting to be filled. In mid-2014, I spent a morning on just the other side of nearby Moffett Field watching a far more somber group of researchers moving out of a suddenly closed division of Microsoft Research.

Some time ago I started a journey into one of the most exciting fields in Computer Science -- Machine Learning. This is my subjective guide for anyone who would like to explore this topic, but don't know how to start. Your first steps should lead to Stanford Machine Learning class at Coursera by Andrew Ng. This course is simply brilliant! Along a way, you will be given everything you need to know, including algebra review.

Artificially intelligent conversational agents have been demonstrated to positively impact team based learning in classrooms and hold even greater potential for impact in the now widespread Massive Open Online Courses (MOOCs) if certain challenges can be overcome. These challenges include team formation, coordination and management of group processes in teams working together while distributed both in time and space. Our work begins with an architecture for orchestrating conversational agent based support for group learning called Bazaar, which has facilitated numerous successful studies of learning in the past including some early investigations in MOOC contexts. In this paper, we briefly describe our experience in designing, developing and deploying agent supported collaborative learning activities in 3 different MOOCs in three iterations. Findings from this iterative design process provide an empirical foundation for a reusable framework for facilitating similar activities in future MOOCs.

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.

A number of people are found in a room and involved in loud conversations in groups, just as it would happen in a cocktail party. There might also be some background noise, which could be music, car noise from outside, etc. Each person in this room is therefore forced to listen to a mixture of speech sounds coming from various directions, along with some noise. These sounds may come directly to one's ear or have first suffered a sequence of reverberations because of their reflections on the room's walls. The problem of focusing one's listening attention on a particular speaker among this cacophony of conversations and noise has been known as the cocktail party problem [6]. It consists of separating a mixture of speech signals of different characteristics with noise added to it. The signals are a-priori unknown (one listens only to a combination of them) as is also the way they have been mixed. The above scenario is a good analog for many other examples of situations that demand for a separation of mixed signals with no presupposed knowledge on the signals and the system mixing them.