Statistical Learning 101: Regression vs. Classification
Dr. Ergun Simsek
Co-director, Computer Science and Information Technologies Department
Bahcesehir University, DC campus
11:30-12:30pm Wednesday, 5 September 2018, ITE 325b, UMBC
In the last decade, statistical learning, which is the concept of using algorithms to identify patterns and/or make predictions based on input data sets, has received increased interest due to its potential to answer diverse questions in various industries such as finance, business, and health. In this talk, I will introduce the two most fundamental methods of statistical learning that are applicable to both data and computer science: regression and classification. Real world examples will be provided to highlight the differences and similarities between these two methods and place them into appropriate contexts.
Dr. Ergun Simsek earned his PhD from Duke University in 2006. He is the co-director of the Computer Science and Information Technologies Department at the Bahcesehir University’s newly established DC campus (BAU DC). He joined Bahcesehir University’s Electrical and Electronics Engineering Department and later was promoted to associate professor. Upon moving back to the United States, he spent six years at the George Washington University’s Department of Electrical & Computer Engineering before returning to BAU at their DC campus. Dr. Simsek’s private sector experience includes working for Schlumberger Doll Research (SDR) Center’s Math and Modeling Department as a post-doctoral research associate, where he helped develop new numerical techniques for various oil and gas industry applications. He continues researching how to solve emerging engineering problems through efficient and robust computational techniques.
Host: Dr. Richard Forno ()