RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. Established in 1997, the original mission was to field a team of robots capable of winning against the human soccer World Cup champions by 2050. The competition has now grown into an international movement with a variety of leagues that go beyond soccer. Teams compete to make robots for rescue missions, the home, and industry. And it's not just researchers, kids also have their own league.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Text mining (deriving information from text) is a wide field which has gained popularity with the huge text data being generated. Automation of a number of applications like sentiment analysis, document classification, topic classification, text summarization, machine translation, etc has been done using machine learning models. Spam filtering is a beginner's example of document classification task which involves classifying an email as spam or non-spam (a.k.a. Spam box in your Gmail account is the best example of this. So lets get started in building a spam filter on a publicly available mail corpus.

Testing is an often overlooked yet critical component of any software system. In some ways this is more true of models than traditional software. The reason is that computational systems must function correctly at both the system level and the model level. This article provides some guidelines and tips to increase the certainty around the correctness of your models. One of my mantras is that a good tool extends our ability and never gets in our way. I avoid many libraries and applications because the tool gets in my way more than it helps me.

A heavy maintenance superintendent for a surface coal mine in Elgin, Texas, Evans was responsible for figuring out how to patch or replace outdated parts of a field delivery system that ferried coal from the mine to a plant. Each minute of downtime could cost the company as much as $170. Now the third-generation coal miner gets her adrenaline rush sitting indoors on a soft swivel chair, fixing code on a computer screen. The 33-year-old is a data scientist currently doing a paid residency at Galvanize in Austin. "I was an adrenaline junkie," sad Evans of her past career.

If you are lost, you ask Siri for directions. No need to pick up the phone, there's a Domino's Pizza bot that will calm your grumbling stomach. Wondering if you need an umbrella before you leave the house? Ask Alexa about the weather (and if you don't have an umbrella, just ask Alexa to order you one). We are used to interacting with conversational agents in our personal lives, depending on them to execute the mundane tasks that we were required to do ourselves in the past.

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us). Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

Machine Learning has the ability to enhance the role of the buy-side trader; bringing trading and portfolio management into a single function. The trading desk has evolved into a highly specialised function within asset management firms, and they are taking on more portfolio management (PM) responsibilities. Trading desks are now fed with price and liquidity information to support over-the-counter trading, and have highly sophisticated execution capabilities for trading more liquid instruments. Consequently, traders are "enhanced" in their abilities and, with greater amounts of data flowing, they are also able to feed more information into the risk management and portfolio management functions. This adds greater value to the asset manager.

Today, SAP has announced three initiatives that expand and accelerate our machine learning capabilities. First, we are launching SAP Leonardo Machine Learning, an exciting new offering that embeds machine learning into a new wave of applications. Second, we are opening our SAP Leonardo Machine Learning Foundation to the SAP ecosystem via SAP Cloud Platform. And third, SAP has joined the Partnership on AI, a broad collaboration of commercial and non-profit organizations to benefit people and society. SAP Leonardo Machine Learning is part of the new SAP Leonardo portfolio, which combines machine learning, Internet of Things (IoT), blockchain, analytics, Big Data, and data intelligence into a holistic digital innovation system allowing our customers to innovate at scale and redefine their business.

The cloud-computing industry is going through a gradual shift towards becoming an intelligent cloud. While compute, storage, and networking continue to be the revenue spinners for cloud vendors, it is machine learning that is becoming the focal point of the contemporary cloud. Here are five cloud services that are highly influenced by machine learning. Cognitive Computing aims to bring sensory capabilities to applications. It enables apps to see, listen, talk, and even make decisions.