The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks---arguably the most central class of graphical models---especially in what is known as the score-based setting. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. Understanding fundamental aspects of cutting planes and the related separation problem( is important not only from a purely theoretical perspective, but also since it holds out the promise of further improving the efficiency of state-of-the-art approaches to solving BNSL exactly. In this paper, we make several theoretical contributions towards these goals: (i) we study the computational complexity of the separation problem, proving that the problem is NP-hard; (ii) we formalise and analyse the relationship between three key polytopes underlying the IP-based approach to BNSL; (iii) we study the facets of the three polytopes both from the theoretical and practical perspective, providing, via exhaustive computation, a complete enumeration of facets for low-dimensional family-variable polytopes; and, furthermore, (iv) we establish a tight connection of the BNSL problem to the acyclic subgraph problem.

Dates: Self-paced (any time) Description excerpt: Do you want to learn how to harvest health science data from the Internet? Or learn to understand the world through data analysis? Start by learning R Statistics! Learn how to use R, a powerful open source statistical programming language, and see why it has become the tool of choice in many industries in this introductory R statistics course. Advanced A few slightly more advanced topics covering optimization and applied linear algebra.

Machine learning is a computer's ability to learn and adapt without being explicitly programmed. This is a widely useful technology that aids in banking, DNA sequencing, search engines, and myriad other applications. If this sounds like a career you'd be interested in, then you'll want to learn all there is to know about machine learning, and you'll want to start from the groun up. Luckily, Windows Central Digital Offers has three awesome course bundles that'll get you up and running and on your way to programming machine learning and AI -- all for $120! This bundle takes you from the basics of machine learning to some advanced techniques, as well as learning to code with Python.

Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them. The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining). The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.

Mercedes-Benz is one of the first Original Equipment Manufacturers (OEMs) to combine the Google Assistant on Google Home with their vehicles, and signals another step forward in the company's connectivity strategy – seamless and intelligent interaction between the customer's Internet of Things (IoT)/Wearables devices and their Mercedes-Benz Cars. "In the last decade, we've seen a range of benefits when smart technology is combined with transportation," said Sajjad Khan, vice president of digital vehicle & mobility at Daimler. "This newest integration shows just how intelligent the car of the future will be, and we plan to roll out more applications as the year progresses that will make daily life even more accessible and convenient." Arriving in the first months of 2017 Mercedes-Benz customers will be able in specific markets to communicate with their cars through their Google Home. Streamlining the customer's digital lifestyle by connecting the car to the IoT has been a rapid evolution during the last few years.

Virtually everywhere you look, Bay Area tech businesses are running into walls. Smartphones were revolutionary and lucrative, but the U.S. market is saturated, and Apple's iPhone sales have fallen for three quarters. The "app economy" has matured, with more people using existing apps than downloading new ones. And Facebook, which has filled users' news feeds with so many ads it can barely add more, is predicting its revenue growth will slump next year. Silicon Valley needs its next big thing, a focus for the concentrated brain power and innovation infrastructure that have made this region the world leader in transformative technology.

A number of the notable science stories of the past year are, quite literally, out of this world. For me, the story of the year has to be the August discovery of an Earth-like planet orbiting the closest star to our own. The star, Proxima Centauri, is just 4.2 light-years from Earth. The planet circling that star has been named Proxima Centauri b. Proxima Centauri b was discovered by astronomers working on a project called Pale Red Dot, who reported that the planet lies in the star's habitable zone, meaning that it could possess water and, maybe, life.

In a recent survey by Tech Pro Research, only 28 percent of respondents, most of whom were in IT leadership positions, said they have firsthand experience with AI or machine learning. However, if the survey results hold true, the majority of respondents will be using the technologies at work in the next few years. Another interesting findings from this survey was that while 42 percent of respondents said their technical staff lack the skills to implement and support AI and machine learning, 41 percent said that all the work in this area would be done in-house. Thirty-nine percent of respondents said their companies were also still working on selecting AI and machine learning vendors. More findings from the survey are shown below. To get all the data and analysis, download the full report: AI and machine learning in the enterprise: Uses, organizational readiness and vendor choices.

Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples.