Virtual Assistant Summit What impact will predictive intelligence have on business efficiency & personal organization? HACKERS.AI applied Artificial Intelligence Conference Open Data Science Conference Santa Clara Heavily focused on applied data science featuring real world applications. Deep Learning in Healthcare Summit Discover the deep learning tools & techniques set to revolutionise healthcare applications, medicine & diagnostics. Open Data Science Conference London Heavily focused on applied data science featuring real world applications. Machine Intelligence Summit Explore how AI will impact transport, manufacturing, healthcare, retail and more.

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

The field of intellectual property is rapidly evolving, both with respect to the law and the technologies being considered for protection. This session provides a primer about what a patent is, current best practices for protecting speech technologies and defending against assertion, and the recent evolution of intellectual property law in the United States, with emphasis on speech, software user interfaces, and mobile technologies. Fraudsters are using robodialing and ANI spoofing to wreak havoc on call centers. From the illegal practice of toll-free traffic pumping and international revenue-sharing fraud, to the more villainous acts of financial account fraud, identity theft, and drug trafficking, this seminar explores the unusual ways criminals are hacking our businesses. We also examine simple and cost-effective practices to protect our businesses, and our customers.

Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic. So what exactly is "machine learning" anyway?

It is the goal of the proposed workshop to bring together researchers from the fields of machine learning and statistical physics in order to discuss the new challenges originating from dynamical data. Such data are modeled using a variety of approaches such as dynamic belief networks, continuous time analogues of these – as often used for disordered spin systems in statistical physics –, coupled stochastic differential equations for continuous random variables etc. The workshop provides a forum for exploring possible synergies between the inference and learning approaches developed for the various models. The experience from joint advances in the equilibrium domain suggests that there is much unexplored scope for progress on dynamical data.

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.