Darius Grala

Darius Grala (born July 7, 1964 in Słupsk, Poland) is an endurance sports car racing driver. He is currently racing in North America and living in the United States. In 2007, Grala will be one of the founding instructors for Supercar Life. In 2006, Grala was one of the founding instructors of the Ferrari Driving Experience. He will continue working with Ferrari in 2007.

In 2005, while driving for the BMW Southard Motorsports team, he won the Grand-Am Daytona Prototype Sportsman of the Year Award. Grala has been a Daytona Prototype driver since the class debuted in the 2003 Rolex 24 Hours of Daytona. In that first race he ran in the top five through most of the night until a mechanical failure in the fifteenth-hour took him out of the race while running in second place overall.

On June 22, 2003 Grala had his first podium finish in a Daytona Prototype at his home track in Watkins Glen, New York.

Prior to the introduction of the Daytona Prototype class, Grala competed in the Grand-Am GTS class. He got his first Rolex Series win in the GTS Class, also at Watkins Glen International.

Machine learning

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),search engines and computer vision. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.