Machine Learning and Data Science

The Machine Learning and Data Science (MLDS) track of the EAS-PhD is committed to training the next generation of machine learning and data science leaders. This track is designed to instill the theoretical knowledge and practical skills necessary for research and discovery in machine learning and data science fields. The track inherits and further strengthens the interdisciplinary nature of the EAS program naturally since the end goal of machine learning and data science methods is usually to enable automation, extract knowledge, and unleash discovery. MLDS doctoral candidates will be masters of the computational and mathematical foundations of machine learning and data science. They will also be highly competent in developing machine learning and data science algorithms, instituting automation and data policies and ethics.

Core Course Requirements

The following core courses of MLDS provide the mathematical and computer science background for the students in the track:

Advanced Mathematical Analysis and Computational Methods (EAS 501, EAS 502)
One from Mathematics of Deep Learning, Scientific Machine Learning (MTH 601, MTH 602)
High Performance Scientific Computing for Data Science and Machine Learning (EAS 520, DSC 520)
One from Advanced Machine Learning, Advanced Data Mining (CIS 550, CIS 530/DSC 531)

EAS 501 - Advanced Mathematical Methods Credits: 3
EAS 502 - Numerical Methods Credits: 3
EAS 520 - High Performance Scientific Computing Credits: 3
EAS 600 - Dissertation Proposal Preparation Credits: 3
EAS 601 - Pre-Dissertation Research Credits: variable; .00 to 12.00
EAS 602 - Research Ethics Credits: 1
EAS 700 - Doctoral Seminar Credits: 2
EAS 701 - Doctoral Dissertation Research Credits: variable; 1.00 to 12.00
MTH 601 - Mathematics of Deep Learning Credits: 3
or
MTH 602 - Scientific Machine Learning Credits: 3
CIS 530 - Advanced Data Mining Credits: 3
or
CIS 550 - Advanced Machine Learning Credits: 3

Specialization course requirements

A minimum of 18 additional hours of coursework is required for post-baccalaureate students. Course selection is based on the research and career goals of the student, and curricula will vary between students. The coursework must include courses from at least two disciplines. These courses are usually taken in mathematics, physics, engineering, or computer science.

Major specialization electives

Choose 4 (four) from CIS/MTH Graduate Course offerings.

Elective major courses provide students the opportunity to obtain depth in his/her focus area. Students must take four courses (12 credits) from within MTH (e.g. MTH 522, 561, 572 – 575) or CIS (e.g. CIS 522, CIS 530, CIS 550, CIS 552, CIS 568, CIS 569).

Minor specialization electives

Choose 2 (two) of COE/BIO/CHM Graduate Course offerings.

Elective minor courses provide the students the opportunity to round their education and gain further inter-disciplinary skills. Students must take two courses (six credits) in one or more of the following programs: Biology, Chemistry, Physics, CoE departments outside of CIS, or other programs approved by the faculty advisor.

PhD Exams

Ph.D. Qualifying Examination (QE) and Comprehensive Exam: Each student must pass a qualifying exam and a comprehensive exam on research preparedness prior to becoming a doctoral candidate. Due to the interdisciplinary nature of the program, courses from the same discipline cannot be used as both major and minor electives. For example, if any MTH courses are used as major electives, MTH courses cannot be used as minor electives.