Milwaukee School of Engineering Masters in Machine Learning Degree

Program Overview

Engineering

The Master of Science in Machine Learning (MSML) at Milwaukee School of Engineering (MSOE) is a fully online, 32-credit graduate degree designed for working professionals.

Program Format

This program delivers live, synchronous instruction two evenings per week and is tailored for those with a technical background in computer science, engineering, or a related field. Students complete eight courses covering core machine learning topics, including Applied Machine Learning, Deep Learning, Machine Learning Production Systems, and AI Ethics and Governance.

The flexible course structure allows students to finish the degree in as little as 12–18 months, taking one or two courses per semester year-round.

Curriculum

The MSML program features a hands-on, industry-driven curriculum supported by ROSIE, MSOE’s GPU computing cluster. Students gain advanced experience in developing, deploying, and optimizing machine learning models in real-world settings.

All students complete seven required courses and one elective, with topics available in reinforcement learning, computer vision, natural language processing, and distributed systems.

Coursework and options include the following:

CSC 5201 – Microservices and Cloud Computing introduces students to the design and deployment of cloud-native microservices using containerization, REST APIs, and distributed storage. Students engage in hands-on labs and a final project to build scalable applications with continuous integration and deployment practices. Topics include event-driven architecture, distributed systems, orchestration tools, and cloud service models.

CSC 5610 – AI Tools and Paradigms focuses on tools and methods used in modern data science and machine learning. Students use Python, Jupyter Notebooks, and libraries for data manipulation, visualization, and predictive modeling. Key concepts include logistic regression, SVMs, Random Forests, data encoding, experimental design, and ethical considerations in AI.

CSC 6605 – Machine Learning Production Systems teaches students to build end-to-end ML-powered services, including data processing, model training, serving, and monitoring. The course covers batch and stream processing, system design patterns, model deployment, and performance evaluation. Emphasis is placed on creating scalable, production-grade machine learning systems.

CSC 6621 – Applied Machine Learning expands on foundational ML concepts, introducing deep learning, reinforcement learning, and agent-based frameworks. Students apply techniques to complex data, use modern libraries, and complete projects involving image and text analysis. Emphasis is placed on real-world applications and technical communication.

CSC 7901 – Machine Learning Capstone serves as a culminating project where students apply machine learning knowledge to solve a complex problem. With faculty mentorship, students complete a project involving analysis, design, implementation, and ethical evaluation. Final deliverables include a written report and presentation.

MTH 5810 – Mathematical Methods for Machine Learning reviews linear algebra and multivariate calculus needed for graduate-level ML study. Topics include matrix operations, vector spaces, optimization, gradients, partial derivatives, and singular value decomposition. Students build mathematical foundations for interpreting ML literature and algorithms.

PHL 6001 – AI Ethics and Governance explores ethical challenges in digital and AI technologies, emphasizing professional and societal impacts. Students study ethical frameworks, data privacy, algorithmic bias, and regulation. Course content fosters critical thinking, professional integrity, and governance solutions for AI systems.

BME 5210 – Medical Imaging Systems introduces imaging modalities such as MRI, CT, ultrasound, and nuclear medicine, along with image processing fundamentals. Students analyze system physics, perform lab-based exercises, and review machine learning applications in medical imaging. Graduate students complete additional research assignments.

BUS 6141 – Analytics Leadership and Strategy covers strategic analytics leadership, organizational maturity models, and ethical decision-making in analytics. Students create and present a strategic analytics plan aligned with business objectives. Topics include the DELTA framework, culture building, and life-cycle management.

CSC 5120 – Software Development for Machine Learning develops intermediate software engineering skills focused on data and AI applications. Students write modular code, implement data structures, use Git, write unit tests, and analyze algorithms. Python is used extensively to prepare students for ML coursework.

CSC 5241 – GPU Programming teaches parallel programming using CUDA and GPU architectures. Students implement algorithms for linear algebra, image processing, and scientific computing while learning optimization techniques. The course ends with a team project and performance comparisons using CUDA libraries.

CSC 5601 – Theory of Machine Learning provides a rigorous introduction to ML algorithms and theory. Students analyze models such as SVMs, decision trees, and logistic regression, using calculus and linear algebra. Topics include optimization, overfitting, and geometric interpretations of decision boundaries.

CSC 5611 – Deep Learning focuses on the design, training, and evaluation of deep neural networks. Topics include backpropagation, CNNs, GANs, transfer learning, and architectural comparisons. Students build and compare implementations to existing libraries and consider ethical implications of deep learning.

CSC 5631 – Artificial Intelligence introduces AI concepts including search algorithms, agent frameworks, logic planning, and nature-inspired techniques. Students apply AI tools to real-world problems and explore evolutionary robotics and heuristic problem-solving methods. Theoretical and practical AI techniques are integrated throughout.

CSC 5651 – Deep Learning in Signal Processing explores the intersection of DSP and deep learning with applications in audio, video, and medical imaging. Students use spectrograms, convolutional layers, and recurrent networks to analyze and classify signals. Labs include training pipelines and term projects.

CSC 5661 – Reinforcement Learning teaches students to design, train, and evaluate AI agents using RL algorithms. Topics include MDPs, simulators, exploration-exploitation tradeoffs, and performance metrics. Students complete a hands-on project to apply RL techniques to a selected control problem.

CSC 5980 – Topics in Computer Science offers the opportunity to explore emerging computer science topics not covered in the standard curriculum. Content varies based on current trends and student-faculty interests, providing flexibility for specialized exploration.

CSC 5981 – Topics in Computer Science with Laboratory allows students to study and apply emerging CS topics through lectures and lab work. The course adapts to faculty and student interests and includes practical, project-based learning opportunities.

CSC 6711 – Recommendation Systems covers algorithms, evaluation metrics, and architectures used in systems like online stores and streaming platforms. Students study candidate generation, filtering, scoring, and ranking, culminating in a project using public datasets and modern frameworks.

CSC 6712 – Distributed Storage Systems explores scalable architectures and algorithms for high-throughput and fault-tolerant data storage. Students build distributed file systems and learn about consensus protocols, replication strategies, and CAP theorem tradeoffs. A term project reinforces design and implementation skills.

CSC 6980 – Topics in Computer Science is a flexible course for examining timely and advanced topics in CS. Subjects are chosen collaboratively between faculty and students, fostering customized learning experiences on cutting-edge developments.

More curriculum info here: https://catalog.msoe.edu/preview_program.php?catoid=43&poid=2199&returnto=1541

Admissions Requirements

Applicants must hold a bachelor’s degree in a technical field, demonstrate programming proficiency (e.g., Python or C++), and have completed coursework in calculus and statistics.

Tuition

Tuition is $1,656 per credit, totaling approximately $52,992 for the full program. Small class sizes and direct faculty access enhance the online learning experience, with personalized support and industry-relevant instruction.

See the official tuition page for more details: https://www.msoe.edu/admissions-aid/tuition-fees/graduate-tuition/

Other Programs

MSOE also offers two stackable graduate certificates—Applied Machine Learning and Machine Learning Engineering—built directly into the MSML pathway.

Career Outcomes

Graduates of MSOE’s MSML program are equipped to become machine learning engineers, AI developers, and data science leaders. They learn to analyze complex problems, design production-ready ML systems, and communicate results effectively to technical and non-technical audiences.

Ideal Candidates

The MSOE Master of Science in Machine Learning (MSML) program is a strong fit for professionals aiming to specialize in the following areas within AI and technology:

  • Machine Learning Engineering – designing, deploying, and maintaining machine learning systems in production.
  • Applied AI Solutions – applying machine learning to real-world problems in business, healthcare, manufacturing, or engineering.
  • Data Science and Predictive Analytics – analyzing complex datasets, building predictive models, and delivering data-driven insights.
  • Deep Learning and Computer Vision – developing neural networks for image recognition, video analysis, and signal processing tasks.
  • AI in Software Development – integrating machine learning algorithms into cloud-based applications and microservices architectures.
  • AI for Edge and Embedded Systems – implementing efficient ML models in hardware-constrained environments using tools like GPU programming.
  • Ethical AI and Governance – addressing fairness, accountability, and transparency in AI system design and deployment.

It is particularly well-suited for those with a technical background who want to become lead architects or developers of AI-driven technologies.