Electrical and Computer Engineering: Machine Learning and Signal Processing, M.S.
Program Overview
The Master of Engineering in Machine Learning and Signal Processing (MLSP) program at UW-Madison is a 12-16 month course-only program designed for students seeking an advanced entry into a data science career in industry. The program provides a practical focus through a hands-on project requirement and a curriculum that draws upon foundational and cutting-edge methods in MLSP. Graduates will be prepared to immediately contribute in a variety of different jobs across data science, machine learning, and signal processing.
Program Outline
Degree Overview:
This program is intended for students seeking 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 UW–Madison 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. 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 within 16 months and enter the workforce, then the MLSP program is right for you.
Outline:
The following required courses must be completed: E C E 610 Seminar in Electrical and Computer Engineering (1 credit) Choose one of the following hands-on project requirement courses: E C E 697 Capstone Project in Machine Learning and Signal Processing E C E 702 Graduate Cooperative Education Program At least one course in Machine Learning: E C E/COMP SCI/M E 532 Matrix Methods in Machine Learning E C E/COMP SCI/M E 539 Introduction to Artificial Neural Networks E C E/COMP SCI 561 Probability and Information Theory in Machine Learning E C E/COMP SCI 760 Machine Learning E C E/COMP SCI 761 Mathematical Foundations of Machine Learning E C E/COMP SCI/STAT 861 Theoretical Foundations of Machine Learning At least one course in Signal Processing: E C E 431 Digital Signal Processing E C E/COMP SCI 533 Image Processing E C E 734 VLSI Array Structures for Digital Signal Processing E C E 735 Signal Synthesis and Recovery Techniques E C E 738 Advanced Digital Image Processing At least 15 credits from the following: E C E 431 Digital Signal Processing E C E 436 Communication Systems I E C E 437 Communication Systems II E C E/COMP SCI/I SY E 524 Introduction to Optimization E C E/COMP SCI/M E 532 Matrix Methods in Machine Learning E C E/COMP SCI 533 Image Processing E C E/COMP SCI/M E 539 Introduction to Artificial Neural Networks E C E/COMP SCI 561 Probability and Information Theory in Machine Learning E C E 601 Special Topics in Electrical and Computer Engineering E C E 717 Linear Systems E C E 719 Optimal Systems E C E 729 Information Theory E C E 730 Probability and Random Processes E C E 734 VLSI Array Structures for Digital Signal Processing E C E 735 Signal Synthesis and Recovery Techniques E C E 736 Wireless Communications E C E 738 Advanced Digital Image Processing E C E/COMP SCI 760 Machine Learning E C E/COMP SCI 761 Mathematical Foundations of Machine Learning E C E 817 Nonlinear Systems E C E 830 Estimation and Decision Theory E C E/COMP SCI/STAT 861 Theoretical Foundations of Machine Learning E C E 901 Special Topics in Electrical and Computer Engineering Additional courses from the previous list, or up to 9 credits of relevant coursework numbered 300 or above in other departments with approval from faculty advisor (Typically in COMP SCI, MATH, STAT, or E P D (Engineering Professional Development))
Careers:
Students who complete this program will be prepared for careers in data science, machine learning, and signal processing. They will be able to work in a variety of industries, including technology, finance, healthcare, and manufacturing.
