What Is a Master of Engineering with a Concentration in AI and Machine Learning?

You can learn how to use a machine learning model without fully understanding how it works. But when a model produces an unexpected result, you need more than the ability to run it. You need to understand what the model is doing, how to evaluate its results, and what may be causing the problem.

That deeper understanding is one reason engineers pursue graduate study in artificial intelligence and machine learning. A Master of Engineering with a concentration in AI and Machine Learning can help professionals build the knowledge to work with these technologies with greater depth and confidence.

Key Takeaways

• What Is a MEng With a Concentration in AI and Machine Learning?
• What Will You Learn in the Program
• Why Does the Math Behind AI and ML Matter?
• How Hands-On Is the Program?
• Is the MEng Program Right for You?

A Master of Engineering with a concentration in AI and Machine Learning is a graduate degree focused on engineering practice rather than academic research. Unlike a research-focused master’s degree, an MEng does not require a thesis. Instead, students complete coursework that focuses on developing technical knowledge and applying it to real engineering problems.

The curriculum covers the mathematical foundations of AI and machine learning, as well as topics such as deep neural networks, computer vision, and natural language processing. The program also includes leadership development, allowing students to develop skills beyond the technical side of AI and ML.

MEng at a Glance

• Format: 100% online
• Total courses: 9
• Total credit hours: 36
• Course length: Accelerated eight-week terms
• Completion time: As few as 12 months

The MEng curriculum gives students a broad foundation for working with various AI and ML approaches, applying them to different types of data, and developing applications that use these technologies.

Key areas of study include:

• Artificial intelligence and machine learning: Explore approaches such as search, reinforcement learning, probabilistic models, game theory, classification, and clustering, and learn how to evaluate their performance.
• Deep neural networks: Learn how neural networks recognize patterns in data, including images, video, speech, and text.
• Computer vision and image analysis: Study methods for analyzing visual information, including image enhancement, segmentation, feature extraction, and object recognition.
• Natural language processing: Explore techniques that allow computers to work with and process human language.
• Innovation and engineering management: Learn how to identify problems, develop solutions, and manage engineering projects.
• Engineering law: Study legal topics relevant to engineering, including professional ethics, intellectual property, contracts, and issues surrounding emerging technologies such as AI and ML.

With pre-built models and AI tools readily available, it can be tempting to focus on how to use them rather than understanding what is happening underneath. UIC takes an approach that connects the mathematical foundations of AI and machine learning with their practical application. Professor Saha Aadirupa describes the program as a way to “bridge the gap between mathematical foundations and real-world AI engineering,” with math and coding reinforcing each other rather than being treated as separate areas of study. Saha teaches MENG 490: Math Preparation for AI and Machine Learning, in which students apply concepts such as calculus and linear algebra to machine learning problems using Python. As she explains, students “don’t just learn calculus and linear algebra in the abstract” but implement techniques such as gradient descent, SVD, and probabilistic models.

The goal is to help students understand not only how an ML algorithm works, but also “what to fix if it does not.” In Saha’s words, “the math and the code reinforce each other at every step,” giving students a stronger foundation for working with AI and machine learning.

Students in the MEng program have opportunities to apply what they learn to problems and projects rather than keeping the coursework entirely theoretical. In MENG 416: Intro to Machine Learning, students apply machine learning techniques to a broad range of data sets and evaluate how well different approaches perform.

In MENG 407: Innovation Tools and Methods, Professor Mark Hallenbeck has students complete a semester-long project based on a problem they choose. Students identify a need, explore possible solutions, test their ideas, and consider what already exists in the market. Hallenbeck has seen students take those projects beyond the course. One group developed an idea for an AI-based holistic health tracker and, six months after completing the course, returned with a pitch deck to get his feedback. The project gives students a chance to work through the same process they might encounter when developing an idea outside the classroom.

Professor Ian Kash also emphasizes that students are learning more than the tools being used today. The program combines hands-on experience with “the key ideas and theories behind them,” he says, so students can apply what they learn beyond the current generation of AI technologies.

UIC’s Online MEng program may be a good fit for students with a background in engineering or a related field who want to develop their knowledge of AI. The program is also open to students with bachelor’s degrees in fields such as biology, chemistry, computer science, mathematics, and physics, as long as they meet the required math and science prerequisites.

You may want to consider the MEng if you:

• Want to understand AI and ML beyond the basic tools: Build a stronger understanding of the mathematics, concepts, and techniques behind AI and machine learning.
• Want hands-on experience: Work with AI technologies and data rather than learning about the field only through theory.
• Want skills that can adapt as AI changes: Develop a foundation in the ideas behind AI, not just the specific tools being used today.
• Want to apply AI across industries: Explore careers in AI ranging from healthcare and finance to transportation and manufacturing.
• Want to continue working while earning your degree: Complete the program entirely online, with accelerated eight-week terms, and full- or part-time pacing.

If you’re unsure whether your previous coursework meets the requirements, contact an enrollment specialist to review your academic or professional experience.

Explore UIC’s Online Master of Engineering with a concentration in AI and Machine Learning to learn more about the program, curriculum, and admission requirements.

• Visit the Program Page