When Liz Wessel was a sophomore at the University of Pennsylvania, she received an unexpected email that would help shape her career, even if she didn't know it at the time. The message didn't come from a professor or advisor, though. It came from beverage giant Anheuser-Busch. The company wanted Wessel to be a campus ambassador, a role that involved promoting its mechanical engineering openings to fellow students. "I thought it was crazy that Anheuser-Busch needed a sophomore to help them with hiring mechanical engineering students for their full time jobs," she says.

A computing educator has to balance teaching efficiently and motivating the student. Efficient teaching means teaching abstractly, emphasizing practice, and preferring direct instruction over having students "figure it out." Motivating the student means giving the students authentic situations, real-world complexity, and reasons to practice. I recently wrote an essay describing this tension (http://bit.ly/2nFRuGZ). Herbert Simon (one of the three authors of the Science article first answering the question "What is Computer Science?"; http://bit.ly/2nFIzpf)

When you played through Super Mario Bros. or Doom for the very first time, chances are you didn't try to speedrun the entire game but instead started exploring -- this despite not really knowing what to expect around the next corner. It's that same sense of curiosity, the desire to screw around in a digital landscape just to see what happens, that a team of researchers at UC Berkeley have imparted into their computer algorithm. And it could drastically advance the field of artificial intelligence. Google's AlphaGo AI, the one that just repeatedly dominated the world's top Go players, uses what's called a Monte Carlo tree search function to decide its next move. Each "branch", or decision, in that tree has a weighted value that's determined from previous experiences and the relative rewards associated with them.

In my recent blogs, I have written about automation tying the network to other domains of IT, and how it's a capability available today that you should start using. Machine learning is another hot topic. While the timeline is several years out for many machine learning applications in networking, it has the potential to be one of those rare technologies that comes along every few decades and fundamentally transforms how networks run. After all, leading companies such as Amazon, Apple, Facebook, Google and Baidu are already transforming their products and business processes with machine learning. Hopefully, as the technology matures, much of the inner workings of it will be deep inside the systems and clouds of your vendors.

Developing machine learning solutions that give a lift from your existing prediction algorithms is not an easy task. They require a multitude of activities to get it right including cleaning up the data, setting up the infrastructure, testing & re-testing the model & finally deploying the algorithm. Here are five machine learning services that can help reduce the pain of deploying your machine learning solution. Based on Microsoft's Azure Cloud Platform, Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments. Machine Learning Studio features a library of time-saving sample experiments, R and Python packages and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Azure ML also supports R and Python custom code, which can be dropped directly into your workspace.

We talk about artificial intelligence (AI), robots, and machine learning as if they're coming soon, or are just some tech pipe dream. In fact, a special report from Bank of America, Merrill Lynch predicts the global market for AI and robots will be just under $153 billion by 2020, and some industries will experience up to a 30% productivity increase through the use of those technologies alone. That can either terrify you if you've seen too many sci-fi films, or excite you if you consider the upside and benefits it could yield. The reality probably lies somewhere in the middle. There will be disruption – there will be jobs and perhaps even whole industries that see massive displacement from robots and other "intelligent" machines. And that says nothing of the inherent risk associated with creating something capable of logical thinking without emotion. The robots may not rise up and exterminate humanity any time soon, but the development of true AI is closer than you think.

Teen scientists use machine learning and neural networks to detect and diagnose diseases, track space debris, design drones and justify conclusions at Intel ISEF 2017. While sentient computer beings like HAL from the classic 2001: A Space Odyssey or Samantha from the 2013 film Her may still be on the distant horizon, some forms of artificial intelligence (AI) are already improving lives. At the 2017 Intel International Science and Engineering Fair (ISEF) – where nearly 1,800 high school students gathered to present original research and compete for more than $4 million in prizes – the next generation of scientists used machine learning and artificial neural networks to find solutions to some of today's most vexing problems. "AI is critical to our future," said Christopher Kang, a budding computer scientist from Richland, Washington, who won an ISEF award in the robotics and intelligent machines category. "Humans have a limit as to how much data we can analyze," he said.

This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones. The proposed UL layers can play two roles: they can be the cost function layer for providing global training signal; meanwhile they can be added to any regular neural network layers for providing local training signals and combined with the training signals backpropagated from upper layers for extracting both slow and fast changing features at layers of different depths. Therefore, the UL layers can be used in either pure unsupervised or semi-supervised settings. Both a closed-form solution and an online learning algorithm for two UL layers are provided. Experiments with unlabeled synthetic and real-world videos demonstrated that the neural networks equipped with UL layers and trained with the proposed online learning algorithm can extract shape and motion information from video sequences of moving objects. The experiments demonstrated the potential applications of UL layers and online learning algorithm to head orientation estimation and moving object localization.

A new report predicts that artificial intelligence (AI) in the U.S. education sector will grow 47.5 percent through 2021. The report, Artificial Intelligence Market in the U.S. Education Sector 2017-2021, is based on in-depth market analysis with inputs from industry experts. One of the major trends surrounding AI and education is AI-powered educational games. Because games have the potential to engage students while teaching them challenging education concepts in an engaging manner, vendors are incorporating AI features into games to enhance their interactivity. Educational games that include adaptive learning features give students frequent and timely suggestions for a guided learning experience.

Includes key concepts of statistical analysis - Probability theory, Types of distribution, Central limit theorem, Hypothesis testing, Statsistical inference.