In our current digital age, a wealth of data is available at our fingertips. Often, the value of this 'Big Data' is not in the data itself, but the ability to learn from historical data in order to make predictions. Machine Learning, a branch of artificial intelligence, involves the development of mathematical algorithms that discover knowledge from specific data sets, and then "learn" from the data in an iterative fashion that allows predictions to be made. Today, Machine Learning has a wide range of applications, including natural language processing, search engine function, medical diagnosis, credit card fraud detection, and stock market analysis. This symposium — part of an ongoing series presented by the Machine Learning Discussion Group at the New York Academy of Sciences — will feature Keynote Presentations from leading scientists in both applied and theoretical Machine Learning. Speakers include Rayid Ghani (University of Chicago), former Chief Data Scientist for the Obama for America 2012 re-election campaign; IBM specialist in automatic speech recognition, Brian Kingsbury; and Northwestern University's Jorge Nocedal, who shall discuss the role of machine learning in optimization.