History of the Machine Learning Department at CMU

The Machine Learning Department at Carnegie Mellon University was founded in the spring of 2006 as the world’s first machine learning academic department. It evolved from the Center for Automated Learning and Discovery (CALD), which was created in 1997 to bring together an interdisciplinary group of researchers with a shared interest in statistics and machine learning.

The first collection of CALD faculty participants came primarily from the Statistics Department and departments within the School of Computer Science. While Statistics Professor Stephen Fienberg and Computer Science Professor Tom Mitchell were the primary faculty involved in creating the center, it also included faculty from philosophy, engineering, business and biological sciences. 

CALD launched its first academic offering in 1999, a master’s degree in knowledge discovery and data mining. In 2002, the center introduced a Ph.D. program in computational and statistical learning, while simultaneously converting the master’s program into a secondary master’s available only to CMU Ph.D. students. Once CALD began to offer educational programs, it also began hiring its faculty.

By spring of 2006, CALD petitioned the university to change convert the center into the Machine Learning Department. In creating the department in 2006, CMU signaled both its belief that machine learning forms a field of enduring academic importance and the university's intention to be a leader in shaping this rapidly developing field.

The Machine Learning Department's research strategy aims to maintain a balance between research into the pure statistical-computational theory of machine learning, and research that invents new algorithms and problem formulations relevant to practical applications.

Our Ph.D. and master’s programs were among the first degree programs in the world to offer specialized training in the field. Today, we offer a Ph.D. in machine learning, as well as joint Ph.D. programs in statistics and machine learning; machine learning and public policy; and neural computation and machine learning. We also offer an undergraduate minor in machine learning and primary and secondary master's degrees.

Our mission is to help lead the development of the discipline of machine learning by performing leading research in the field; by developing and propagating a model academic curriculum for the field; and by helping society benefit from the knowledge gained by the field.