Machine learning, the study of computer algorithms that improve automatically through experience, has a wide spectrum of applications, including natural language processing, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, and stock market analysis.
The Machine Learning Discussion Group at the New York Academy of Sciences holds an annual symposium each fall to discuss advanced research related to such topics. The aim of this series is to continue to build a community of leading scientists in machine learning from the New York City area's academic, government, and industrial institutions by convening and promoting the exchange of ideas in a neutral setting. Top scientists in both applied and theoretical machine learning are invited to present their research.
In addition, several submitted abstracts will be selected for oral presentations as well as for presentation as papers in the poster session. Based on these "Spotlight" talks, a "best student paper" will be chosen. The student winner will be announced at the end of the day-long symposium.
The symposium will be followed by a series of short presentations by tech startups, sponsored by hackNY, an organization that connects math and computer science students with emerging enterprises. Attendance is open to all but space is limited.
|Student / Postdoc / Fellow Member
|Student / Postdoc / Fellow Nonmember
|Nonmember Not for Profit
Past Machine Learning Symposia