Master of Science, Machine Learning and Signal Processing
The Signal Processing and Machine Learning MS program educates students in the foundations of data science theory and methods. Graduates of this program will be poised to immediately participate in data analysis tasks in a variety of application domains using tools based in linear algebra, statistics, and optimization. The coursework includes a summer practicum that gives students hands-on experience with real-world datasets.
Is This Program Right For You?
The Machine Learning and Signal Processing (MLSP) program is intended for students looking for a jump-start on a career in data science, with a passion for quantitative thinking, practical problem solving, computer programming, and applications to a variety of domains. It is designed for motivated students ready for the rigors of a 12-month accelerated program.
The required coursework draws upon both classical and modern methods in MLSP, and is taught by faculty conducting cutting-edge MLSP research. Successful students will have some experience with linear algebra, statistics, and computer 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 MLSP jobs.
The focus of the MLSP program differs from the standard research-based MS program by the replacing the independent research that leads to a written thesis with an accelerated coursework plan, the summer practicum, and a focus on courses in the MLSP area. If you are interested in research and advanced concept development, you are better served pursuing a research-focused MS program. If you want to complete your degree in 12 months and be part of data science in the workforce, then the MLSP program is right for you.
For more detailed information, please visit the program website.
What You Learn
- Demonstrate a strong understanding of mathematical, scientific, and engineering principles in the MLSP field
- Demonstrate an ability to formulate, analyze, and independently solve advanced MLSP problems
- Apply the relevant scientific and technological advancements, techniques, and engineering tools to address these problems
- Recognize and apply principles of ethical and professional conduct
At A Glance
|Delivery||In class instruction|
|Credits||30 graduate credits|
|Time Frame||Completion of program may be done within 1 calendar year starting Fall semester only|
|Tuition||Resident: $5,994/semester + $2,986 for 6 summer credits (Fall 2017 information)|
|Nonresident: $12,658/semester + $6,318 for 6 summer credits (Fall 2017 information)|
|Degree Conferred||Master of Science in Electrical Engineering|
|Offered By||The UW-Madison College of Engineering|
|Application Deadlines||Fall 2019: March 15, 2019|
Degree & Prospective Student Information
- 30 credit degree program; With program approval, students may count graduate coursework from other institutions toward the minimum graduate degree credit requirement and the minimum graduate coursework (50%) requirement. No credits from other institutions may be counted toward the minimum graduate residence credit requirement. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.
- UW-Madison students completing their Bachelor’s degree in the Electrical & Computer Engineering department at UW-Madison With program approval, up to 7 credits numbered 400 or above can be counted toward the minimum graduate degree credit requirement. Up to 7 credits of ECE courses numbered 700 or above can be counted toward the minimum graduate coursework (50%) requirement. No credits can be counted toward the minimum graduate residence credit requirement.
- Undergraduate Work from Other Institutions: With program approval, students may count up to 7 credits of undergraduate coursework from a bachelor of science degree in Electrical Engineering, Computer Engineering, Electrical and Computer Engineering, Electrical Engineering and Computer Science, or Computer Science from an ABET-accredited program at other institutions toward fulfillment of minimum degree requirements. No credits from other institutions can be counted toward the minimum graduate residence credit requirement. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements. The total of the credits from graduate work from other institutions, undergraduate work from other institutions, and UW-Madison undergraduate work cannot exceed 7.
- Half of degree coursework (15 out of 30 total credits) must be graduate coursework. Must maintain 3.00 GPA to remain in the program. A grade of B or better in any graduate course is acceptable. A grade of BC in an ECE course is acceptable, provided the total cumulative GPA for graduate ECE courses is greater than or equal to 3.00. A grade of BC or C in a non-ECE course is acceptable only if approved by the Graduate Committee. With program approval, students are allowed to count no more than 9 credits of graduate coursework from other institutions. Coursework earned five or more years prior to admission to a master’s degree is not allowed to satisfy requirements.
- Exceptions to these requirements may be requested to the appropriate ECE department committee and considered on a case-by-case basis.
Applicants must first meet all of the requirements of the Graduate School.
Please visit https://grad.wisc.edu for details.
Applicants must also meet department specific requirements as outlined below:
- Must have a bachelor’s degree or expect to earn a bachelor’s degree before their first semester in the program
- Submit a Statement of Purpose
- Submit 3 letters of recommendation
- Non-native English speakers must have a Test of English as a Foreign Language (TOEFL) with a minimum score of 580 (written), 243 computer-based test), or 90 (Internet version).
- Scores from one these exams are required unless you met one of the following exemptions:
- English is the exclusive language of instruction at the undergraduate level
- You earned a degree from a regionally accredited U.S. college or university not more than 5 years prior to the anticipated semester of enrollment
- You completed at least two full-time semesters of graded course work (excluding ESL courses) at an institution where English is the exclusive language of instruction, not more than 5 years prior to the anticipated semester of enrollment
Information for Current Students
Click HERE to view or download the most current ECE Graduate Student Handbook.
Fall Semester (14 credits) – choose at the minimum four courses from the list below
- ECE 431 (3 credits): Digital Signal Processing
- ECE 436 (3 credits): Communication Systems
- ECE 524 (3 credits): Introduction to Optimization
- ECE 532 (3 credits): Matrix Methods in Machine Learning
- ECE 533 (3 credits): Image Processing
- ECE 539 (3 credits): Introduction to Artificial Neural Network and Fuzzy Systems
- ECE 717 (3 credits): Linear Systems
- ECE 729 (3 credits): Theory of Information Processing and Transmission
- ECE 730 (3 credits): Modern Probability Theory and Stochastic Processes
- ECE 761 (3 credits): Mathematical Foundations of Machine Learning
- ECE 901 (3 credits): Special Topics (if approved by program director/advisor)
- EPD 611/612 (3 credits): Technical Project Management
Spring Semester (13 credits) – choose at the minimum four courses from the list below
- ECE 437 (3 credits): Communication Systems II
- ECE 524 (3 credits): Introduction to Optimization
- ECE 719 (3 credits): Optimal Systems
- ECE 735 (3 credits): Signal Synthesis and Recovery Techniques
- ECE 736 (3 credits): Wireless Communications
- ECE 738 (3 credits): Advanced Digital Image Processing
- ECE 830 (3 credits): Estimation and Decision Theory
- ECE 861 (3 credits): Theoretical Foundations of Machine Learning
- ECE 901 (3 credits): Special Topics (if approved by director/advisor)
- EPD 617 (3 credits): Communication Technical Information
Summer (3 credits)
- ECE 697 (3 credits): Directed Project in Signal Processing and Machine Learning
- ECE 702 (up to 2 credits): Co-op
Please DO NOT mail any paper copies of application materials. They will not be reviewed. Please only upload the required application materials with the Graduate School application. This includes official transcripts. If an applicant is admitted by the ECE Admissions Committee, they will receive further instructions from the ECE Graduate Admissions Office.
Applicants should monitor your application status by visiting the “Graduate Application Status” window within your MyUW portal (information on this is received after submitting an application). You may need to activate a NetID to gain access to the MyUW portal.
We anticipate most decisions will be made by mid-March for Fall semester applications. Applicants will receive an e-mail from the ECE Graduate Admissions Office with the Admissions Committee’s decision as soon as the office receives it.