FROM avoiding jaywalkers to emergency braking to eventually, perhaps, chauffeuring the vehicle itself, it is clear that artificial intelligence (AI) will be an important part of the cars of the future. But it is not only the driving of them that will benefit. AI will also permit such cars to use energy more sparingly. Cars have long had computerised engine-management that responds on the fly to changes in driving conditions. The introduction of electric power has, however, complicated matters.

This series of talks is part of the Foundations of Machine Learning Boot Camp. Videos for each talk area will be available through the links above.

When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go. We saw AIs playing video games like Doom and Super Mario.

"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.

Publishing data on Kaggle is a way organizations can reach a diverse audience of data scientists with an enthusiasm for learning, knowledge, and collaboration. For Dr. Erin Miller of START, the National Consortium for the Study of Terrorism and Responses to Terrorism, making her organization's Global Terrorism Database available for analysis by Kaggle users has brought new awareness to their cause. In this Open Data Spotlight, Erin discusses how setting aside agendas and focusing on understanding this unparalleled dataset of over 150,000 attack events allows users to undertake constructive analyses that may defy common conceptions about terrorism. Read on to learn more about the Global Terrorism Database project and the ways users of open data can make valuable contributions to the organizations that make them possible. My role started out (more than 12 years ago) as a graduate assistant cleaning raw data, and now I manage the project team, workflow, resources, and interaction with end users and related research projects.

Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal \'{E}l\H{o} update still remains, after more than 50 years, a valuable baseline which is difficult to improve upon, though in its original form it is a heuristic and not a proper statistical "model". Mathematically, the \'{E}l\H{o} rating system is very closely related to the Bradley-Terry models, which are usually used in an explanatory fashion rather than in a predictive supervised or on-line learning setting. Exploiting this close link between these two model classes and some newly observed similarities, we propose a new supervised learning framework with close similarities to logistic regression, low-rank matrix completion and neural networks. Building on it, we formulate a class of structured log-odds models, unifying the desirable properties found in the above: supervised probabilistic prediction of scores and wins/draws/losses, batch/epoch and on-line learning, as well as the possibility to incorporate features in the prediction, without having to sacrifice simplicity, parsimony of the Bradley-Terry models, or computational efficiency of \'{E}l\H{o}'s original approach. We validate the structured log-odds modelling approach in synthetic experiments and English Premier League outcomes, where the added expressivity yields the best predictions reported in the state-of-art, close to the quality of contemporary betting odds.

If there's one truth that the tech revolution has proven itself repeatedly, it's this: just because we can build something doesn't mean we should. Take, for instance, Google Glass, iTunes Ping, and Clippy (to say nothing of the smart fork.) Likewise, imagine an app that can take a snapshot of a student's homework assignment, chew on the questions using cloud-based artificial intelligence, and spit out the answers. In theory, this sounds great for students, yet terrible for learning -- that is, until you put Socratic to the test. "Every student today goes to the Internet, goes to Google, to ask all of their questions -- this is something that's happening anyway," says Shreyans Bhansali, the co-founder and head of engineering for the free homework-helping app that's currently topping Apple's App Store for education software. "We read the question, we figure out what they need to learn to answer it, and then we teach them that stuff."

DeepTraffic is a gamified simulation of typical highway traffic. Your task is to build a neural agent – more specifically design and train a neural network that performs well on high traffic roads. Your neural network gets to control one of the cars (displayed in red) and has to learn how to navigate efficiently to go as fast as possible. The car already comes with a safety system, so you don't have to worry about the basic task of driving – the net only has to tell the car if it should accelerate/slow down or change lanes, and it will do so if that is possible without crashing into other cars. The page consists of three different areas: on the left you can find a real time simulation of the road, with different display options, using the current state of the net.

Artificial Intelligence, Machine learning and Predictive Analytics are at a perfect storm and many companies leverage these technologies to transform their organization. These technologies existed for a decade, but they evolved rapidly – machine learning today is not like machine learning of the past. In these session, we will look into these technologies, understand what SAP is to offer and how SAP customers are using these technologies. The SAP Community Calls (known previously as "Mentor Monday Webinar Series") is a part of the SAP Mentors Program, and hosted by SAP Mentors, to share relevant and interesting SAP topics and knowledge with all SAP community members. In case of any questions please contact sapmentors@sap.com.

This 3-hour course (video slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Deep learning (aka neural networks) is a popular approach to building machine-learning models that is capturing developer imagination. If you want to acquire deep-learning skills but lack the time, I feel your pain. In university, I had a math teacher who would yell at me, "Mr. Görner, integrals are taught in kindergarten!"