Eight Georgia Tech schools partner to offer advanced degree in emerging field of machine learning
The Georgia Institute of Technology has been approved to offer a new advanced degree program for the emerging field of machine learning.
In a unanimous vote, the Board of Regents of the University System of Georgia approved Georgia Tech’s request to establish a Doctor of Philosophy in Machine Learning.
“The field of machine learning is now ubiquitous in everything we do. It impacts everything from robotics and cybersecurity to data analytics – all topics of extraordinary interest to Georgia Tech,” said Rafael L. Bras, Georgia Tech provost and executive vice president for Academic Affairs and the K. Harrison Brown Family Chair.
“This new Ph.D. program embraces the interdisciplinary impact and nature of machine learning and serves to strengthen Georgia Tech’s strong position as a leading center of knowledge and expertise in this increasingly important field of study.”
A collaborative approach
The machine learning (ML) Ph.D. program is a collaborative venture between the colleges of Computing, Engineering, and Sciences. An inaugural class of approximately 15 students is scheduled to convene for the Fall 2017 semester. The class is expected to comprise incoming Ph.D. students and some who may have recently begun other programs at Georgia Tech.
Qualified students can apply to the program through one of eight participating schools at Georgia Tech. These include the schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing.
Participating schools in the College of Engineering include the School of Electrical and Computer Engineering, the Stewart School of Industrial and Systems Engineering, the Coulter Department of Biomedical Engineering, and the Guggenheim School of Aerospace Engineering.
Students can also apply for the ML Ph.D. program through the School of Mathematics in the College of Sciences.
“The ML Ph.D. degree program is ideal for students from a variety of academic backgrounds interested in multidisciplinary collaboration,” said Justin Romberg, the Schlumberger Professor in the School of Electrical and Computer Engineering and program curriculum coordinator for the ML Ph.D. program.
“Students will learn to integrate and apply principles from computing, statistics, optimization, engineering, mathematics, and science to innovate and create machine learning models and then apply them to answer important, real-world, data-intensive questions.”
Although students apply to the program through one of eight schools, the hub for the new ML Ph.D. degree is the Center for Machine Learning at Georgia Tech (ML@GT).
Opened in July 2016 as the home for machine learning at Georgia Tech, ML@GT has more than 100 affiliated faculty members from five Georgia Tech colleges and the Georgia Tech Research Institute, as well as some jointly affiliated with Emory University.
“While there are many faculty members doing machine learning research at Georgia Tech, until now there has been a lack of a structured and systematic interdisciplinary ML training program for students,” said College of Computing Professor and ML@GT Director Irfan Essa.
“Once accepted to the program, students become members of the ML@GT community, where they will be able to develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline.”
The operations and curricular requirements for the new Ph.D. program – which include five core and five elective courses, a qualifying exam, and a doctoral dissertation defense – will be managed by ML@GT.
The five core courses in the ML Ph.D. degree program are:
- Mathematical Foundations of Machine Learning
- Intermediate Statistics
- Machine Learning: Theory and Methods
- Probabilistic Graphical Models and Machine Learning in High Dimensions
“Our goal is to have students develop a deep understanding and expertise in a specific theoretical aspect or application area of the machine learning discipline,” said Romberg.
“The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance.”
After successfully completing all of the curricular requirements, students will have the computational skills and mathematical modeling skills needed for careers in industry, government, or academia.
“Machine learning is helping industries – from aerospace and biomedicine to cybersecurity and financial services – make sense of data to improve business processes and identify previously hidden connections that benefit their businesses and their customers,” said Essa.
“Beyond this, machine learning is fueling a rapid development of stronger, more robust artificial intelligence applications, like natural language processing, that may help to solve many of the world’s more complex and longstanding problems.”