Adaptive Real Time Machine Learning

• What we'll do
Although machine learning algorithms are widely used in extremely diverse situations, in practice, one or more major limitations almost invariably appear and significantly constrain successful applications. Frequently, these problems are associated with large increases in the rate of generation of data, the quantity of data and the number of attributes to be processed. Increasingly, the data situation (big data) is now beyond the capabilities of conventional machine learning algorithms. The term “Real Time” is used to describe how well an algorithm can accommodate an ever-increasing data load instantaneously. However, such real-time problems are usually closely coupled with the fact that conventional machine learning algorithms operate in a batch mode where having all the relevant data at once is a requirement. The main goal of this meetup is to show how we can apply real time algorithms to real-world problems and save significant amount of time and money.

Saed Sayad has more than 20 years of experience in data science, machine learning and artificial intelligence and has designed, developed and deployed many business and scientific applications of predictive modeling. Saed's main research area is real time machine learning and has been presenting a popular graduate data mining course at University of Toronto since 2001.

• What to bring

• Important to know