Electrical Engineering MS - Machine Learning and Signal Processing

This is a named option in theElectrical Engineering M.S.

The Electrical and Computer Engineering Department (ECE) offers the Electrical Engineering M.S.-Machine Learning and Signal Processing (MLSP) named option program which is intended for students looking for an advanced entry into a data science career in industry.

Students will learn quantitative thinking, practical problem-solving, computer programming, and applications to a variety of domains. It is designed to deepen the student's technical knowledge and sharpen their professional skills for a well-prepared entry into industry. The program provides a practical focus through a course-only curriculum, an accelerated and predictable 16-month completion time, and a professional development hands-on project requirement. Well-prepared students and UWMadison undergraduates may find it feasible to complete the program in 12 months.

The required coursework draws upon foundational and cutting-edge methods in MLSP, and is taught by faculty conducting pioneering research in the field. Successful students will have some experience with linear algebra, statistics, and programming. The combined focus on the mathematical foundations of data science and their practical application to real-world problems will prepare graduates to be ready to immediately contribute in a variety of different jobs across data science, machine learning, and signal processing.

The focus of the MLSP program differs from the traditional research-based M.S. program. MLSP students do not conduct independent research and prepare a thesis, but rather have an accelerated course plan focused in the MLSP area with a professional development hands-on project, either via an internship/co-op or an independent project. Students also have the opportunity to take select courses from Engineering Professional Development. Overall, the MLSP M.S. program requires 30 credit hours, including the hands-on project.If you are interested in research and advanced concept development, you are better served pursuing a research-based M.S. program or a Ph.D. program. If you want to complete your degree in 1216 months and have a fast-track into the data science workforce, then the MLSP program is right for you.

MLSP students cannot be simultaneously enrolled in another graduate program at UWMadison while completing this program.

After completing the program, students will earn a diploma stating Master of Science in Electrical Engineering,” and the transcript will include the indication "Named Option: Machine Learning and Signal Processing."

For more information on this specific degree plan, please visit the ECE website.

A bachelor’s degree from a regionally accredited U.S. institution or a comparable degree from an international institution is required. International applicants must have a degree comparable to a regionally accredited U.S. bachelor’s degree. You must have completed your undergraduate degree, or similar, before starting graduate school.

A minimum undergraduate grade-point average (GPA) of 3.00 on the equivalent of the last 60 semester hours (approximately two years of work) or a master’s degree with a minimum cumulative GPA of 3.00 is required. Applicants from an international institution must demonstrate strong academic achievement comparable to a 3.00 for an undergraduate or master’s degree. The Graduate School will use your institution’s grading scale. Do not convert your grades to a 4.00 scale.

Every applicant whose native language is not English, or whose undergraduate instruction was not in English, must provide an English proficiency test score. TOEFL scores must be submitted electronically via ETS. IELTS scores can be submitted electronically or by paper.  Our office address is: UW-Madison Graduate School, Office of Admissions, 232 Bascom Hall, 500 Lincoln Drive, Madison, WI 53706.  Your score will not be accepted if it is more than two years old from the start of your admission term. Country of citizenship does not exempt applicants from this requirement. Language of instruction at the college or university level and how recent the language instruction was taken are the determining factors in meeting this requirement.

Applicants are exempt if: