Machine Learning Academics
The Machine Learning Department is made up of a multi-disciplinary team of faculty and students across several academic departments. Machine learning is dedicated to furthering the scientific understanding of automated learning, and to producing the next generation of tools for data analysis and decision making based on that understanding.
Today's demand for expertise in machine learning far exceeds the supply, and this imbalance will become more severe over the coming decade.
Students can pursue one of 4 PhD programs, a Master's program, and an undergraduate Minor and Major. Students can also take classes in the Machine Learning Department without being part of one of its academic programs.
The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning and Computational Statistics. The program is operated jointly by faculty in the School of Computer Science and Department of Statistics.
This PhD program differs from the Machine Learning PhD program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics PhD program in its emphasis on machine learning and computer science. The Joint PhD Program in Machine Learning and Statistics is a new program aimed at preparing students for academic careers in both CS and Statistics departments at top universities.
The Joint Ph.D. Program in Machine Learning and Public Policy is a new program operated jointly by faculty in Machine Learning and the Heinz College (Schools of Public Policy, Information Systems, and Management). Students will gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy domains.
This Joint PhD program trains students in the application of Machine Learning to Neuroscience by combining core elements of the ML PhD program and the Program in Neural Computation (PNC) offered by the Center for the Neural Basis of Cognition (CNBC).
The MS in Machine Learning is ideal for students considering a career in industry or as preparation for a PhD. Regardless of the application used, the curriculum and program requirements are the same.
The primary application is for everyone who isn't currently earning a degree from or working at CMU. 99% of applicants use the primary application.
Current CMU undergraduates may be eligible to apply early and earn the MS in their fifth year.
Students currently earning a PhD in another department at CMU and CMU staff and faculty are welcomed to apply using the Secondary MS application.
Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology. The Minor in Machine Learning allows undergraduates to learn about the core principles of machine learning.
This joint major, managed by the Dietrich College of Humanities and Social Sciences, develops the critical ideas and skills underlying statistical machine learning — the creation and study of algorithms that enable systems to automatically learn and improve with experience. It is ideal for students interested in statistical computation, data science, or "Big Data" problems, including those planning to pursue a related PhD or a job in the tech industry.
These courses are being offered by the Machine Learning Department this semester.
Apply to be a Teaching Assistant or Course Assistant in the Machine Learning Department. Both graduate and undergraduate students are welcome to apply.