This guest post was written by Daniel Emaasit, a Ph.D Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's research interests include the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations. Don't miss Daniel's webinar on Model-Based Machine Learning and Probabilistic Programming using RStan, scheduled for July 20, 2016 at 11:00 AM PST. This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice. During my Masters in Transportation Engineering (2011-2013), I used traditional statistical modeling in my research to study transportation related problems such as highway crashes.

Dr. Sven Krasser currently serves as Chief Scientist at CrowdStrike where he leads the machine learning efforts utilizing CrowdStrike's Big Data platform. He has authored numerous peer-reviewed publications and is co-inventor on more than two dozen patented network and host security technologies. Machine learning is presently a hot topic in the security industry. On the one side, we have companies praising machine learning as the panacea solving all of our security needs. On the other side, there are companies seeing no merit in machine learning urging us to stay with so-called proven approaches.

Artificial intelligence is on the lips of every influencer in Silicon Valley. Mark Zuckerberg is building his own artificially intelligent butler, and Elon Musk recently launched an AI company. But AI is on the rise, and it's already driving change in retail and consumer businesses. Several aspects of AI have advanced significantly in recent years, particularly speech recognition and text understanding. Speech recognition, also known as voice understanding, interprets spoken words and matches them with concepts, tasks, and people.

Machine Learning is a science that allows computer systems to learn independently and improve themselves based on past experiences or human input. It might sound like a new technique, but the truth is that some of our most common interactions with our apps and the Internet are driven by automatic suggestions or recommendations, and some companies even make decisions using predictions based on past data and machine learning algorithms. This technology comes in handy specially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates (websites clicks, credit card transactions, GPS trails, social media interactions, etc.), and it's becoming a challenge to process all this valuable information and use it in a meaningful way. This is where rule-based algorithms fall short: machine learning algorithms use all the collected, "past" data to learn patterns and predict results (insights) that helps make better business decisions. Let's take a look at some examples of machine learning.

With the general availability of the "Knights Landing" Xeon Phi many core processors from Intel last month, some of the largest supercomputing labs on the planet are getting their first taste of what the future style of high performance computing could look like for the rest of us. We are not suggesting that the Xeon Phi processor will be the only compute engine that will be deployed to run traditional simulation and modeling applications as well as data analytics, graph processing, and deep learning algorithms. But we are suggesting that this style of compute engine – it is more than a processor since it includes high bandwidth memory and fabric interconnect adapters on a single package – is what the future looks like. And that goes for Knights family processors and co-processors as well as the "Pascal" and "Volta" accelerators made by Nvidia, the Sparc64-XIfx and ARM chips that will be used in the used in the Post-K system in Japan made by Fujitsu, the Matrix2000 DSP accelerator being created by China for one of its pre-exascale systems, or the CPU-GPU hybrids based on its "Zen" Opterons that AMD is cooking up for supercomputing systems in the United States and, with licensing partners, in China. During the recent ISC16 supercomputing conference in Frankfurt, Germany, Intel gathered up the executives in charge of some of the largest supercomputing facilities on the planet who are also – not coincidentally, but absolutely intentionally – also early adopters of the Knights Landing Xeon Phi and, in some cases, the Omni-Path interconnect that is a kicker to Intel's True Scale InfiniBand networking.

Natural Language Understanding (NLU) is a form of artificial intelligence that adds more fuel to the chatbot fire. When users engage in a conversation powered by NLU, the results are generally better. You can change your mind about an original request or even interrupt yourself mid-sentence, and you can use unusual words or phrases. The NLU engine is more like a neural network that understands true intent and meaning, and it can understand meaning from natural words and phrases. Now, a company called Pat is ready to show the world how users can benefit.

A mall security robot has effectively redefined the term artificial intelligence after it barreled into a toddler last week at a northern California shopping center, running him over and leaving him with various bumps and bruises. The robot, weighing in at 300 pounds and standing at 5 feet, is typically an attraction for patrons at the Stanford Shopping Center in Palo Alto. But this time around it was more of an attractive nuisance, the 16-month-old boy's mother told KGO-TV, the local ABC affiliate. "The robot hit my son's head and he fell down facing down on the floor and the robot did not stop and it kept moving forward," said Tiffany Teng said. The runaway robot caused the young boy to experience a sore head, swelling in a foot and bruising on a leg.

U.S. officials covered up the Chinese government's attempt to hack computers used by the nation's banking regulator, Republican lawmakers claimed Wednesday. The report from the U.S. House of Representatives Committee on Science, Space and Technology alleges that the Chinese government was spying on the Federal Deposit Insurance Corporation, which stores confidential data on the nation's largest financial institutions, over a three-year period starting in 2010. "Even the former chairwoman's computer had been hacked by a foreign government, likely the Chinese," the report claims. The intruders were reportedly seeking "economic intelligence," Reuters reported. FDIC officials allegedly tried to cover up the hack to protect the regulator's incoming chairman.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

This is the first of a series of posts discussing some recent experiments combining dask and scikit-learn. A small (and extremely alpha) library has been built up from these experiments, and can be found here. There are several ways of parallelizing algorithms in machine learning. Some algorithms can be made to be data-parallel (either across features or across samples). Many machine learning algorithms have hyperparameters which can be tuned to improve the performance of the resulting estimator.