Program Overview
Electrical and Computer Engineering- Location: Madison, WI
- Length: 12 Months
- Tuition: $33,000
- Website: https://guide.wisc.edu/graduate/electrical-computer-engineering/electrical-computer-engineering-ms/electrical-computer-engineering-machine-learning-signal-processing-ms/
The University of Wisconsin–Madison offers a Master of Science in Electrical and Computer Engineering with a named option in Machine Learning and Signal Processing (MLSP). This is an accelerated, course-only program that prepares students for data science and engineering roles in industry.
The program is designed to be completed in 16 months, though well-prepared students may finish in 12 months. It requires a total of 30 credits and is delivered fully in-person, not online.
Curriculum
The MLSP curriculum includes core coursework in machine learning, signal processing, and applied mathematics, with electives drawn from a broad set of ECE and computer science courses.
MATRIX METHODS IN MACHINE LEARNING
Covers linear algebra concepts central to machine learning, with applications in clustering, classification, denoising, and data analysis. Includes topics such as regression, regularization, SVD, iterative methods, and algorithms like SVMs, neural networks, and deep learning. Prior experience with tools like Python or MATLAB is required.
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
Explores theoretical and practical aspects of neural networks, including multi-layer perceptrons, convolutional networks, and recurrent architectures. Topics include genetic algorithms and evolution computing, with applications in control, pattern recognition, prediction, and tracking.
PROBABILITY AND INFORMATION THEORY IN MACHINE LEARNING
Introduces probabilistic tools and theories used in machine learning, covering classification, entropy, mutual information, and decision theory. Also covers advanced methods such as Bayesian inference, logistic regression, graphical models, expectation maximization, and variational techniques.
MACHINE LEARNING
Covers core computational methods for learning, including inductive inference, analogical reasoning, and neural networks. Focuses on learning theory, algorithm design, and cognitive modeling in intelligent systems.
MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING
Focuses on the mathematical tools that support modern learning algorithms, such as probabilistic modeling, algebraic data structures, geometric representations, and optimization methods. Prior coursework in linear algebra and probability is required.
THEORETICAL FOUNDATIONS OF MACHINE LEARNING
Explores advanced topics such as statistical learning theory, VC theory, high-dimensional models, nonparametric methods, and optimization. Emphasizes theoretical analysis and learning paradigms.
DIGITAL SIGNAL PROCESSING
Introduces techniques for sampling, transforming, and filtering signals. Covers discrete Fourier transforms, fast convolution, z-transforms, and design of digital filters.
IMAGE PROCESSING
Focuses on mathematical modeling and enhancement of digital images. Topics include degradation models, restoration, segmentation, coding, and applications in tomography.
VLSI ARRAY STRUCTURES FOR DIGITAL SIGNAL PROCESSING
Examines architecture and design of VLSI systems for real-time signal processing. Highlights algorithm-to-hardware mapping techniques and requires familiarity with DSP and computer architecture.
SIGNAL SYNTHESIS AND RECOVERY TECHNIQUES
Covers signal creation under design constraints and inverse problem-solving using convex projections. Applications include incomplete data recovery and image reconstruction.
ADVANCED DIGITAL IMAGE PROCESSING
Provides advanced modeling and filtering techniques including Markov fields and anisotropic diffusion. Covers motion estimation, video compression, and restoration tasks; prior image processing knowledge is expected.
COMMUNICATION SYSTEMS I
Introduces modulation techniques and noise analysis in communication systems. Topics include AM, FM, pulse modulation, synchronization, and equalization.
COMMUNICATION SYSTEMS II
Focuses on digital communication system performance. Covers error probability, optimal receiver design, spread-spectrum systems, and channel coding strategies.
INTRODUCTION TO OPTIMIZATION
Presents foundational tools in discrete and continuous optimization. Emphasizes model formulation, algorithm selection, and use of software tools for practical applications.
More information about the curriculum here:
Students must complete a seminar, a capstone or cooperative education project, and at least one course each in machine learning and signal processing. The curriculum is structured for professional application rather than academic research, making it ideal for students seeking immediate industry entry.
Tuition
The total tuition cost for the on-campus MS Information program, which requires 30 credits at $1,100 per credit, is $33,000.
Please note that this figure covers tuition only and does not include additional expenses such as student fees, housing, meals, books, or other living costs.
See the official tuition page for more details:
https://viz.wisc.edu/views/TuitionandSegregatedFeeRatesforAcademicPrograms/HomePage?%3Aembed=y&%3Aiid=1&%3AisGuestRedirectFromVizportal=y
Admissions Requirements
Applicants must submit a statement of purpose, résumé, transcripts, and three letters of recommendation. While the GRE is optional, non-native English speakers must demonstrate proficiency through accepted language tests.
Applications are accepted for fall admission only, with a priority deadline of December 15 and space-available consideration through March 15.
Ideal Candidate and Career Outcomes
The MLSP program is best suited for students with backgrounds in engineering, computer science, or math who have experience with linear algebra, statistics, and programming. It does not involve thesis research, making it a good fit for those focused on entering the workforce quickly. Graduates receive a diploma titled “Master of Science in Electrical and Computer Engineering,” with the named option shown on the transcript. The program prepares graduates for roles in machine learning, signal processing, and broader data science applications.