Cutting-Edge Curriculum
Preparing mechanical engineers for an industry shift to AI
The mechanical engineering industry is experiencing a monumental shift toward artificial intelligence. At Carnegie Mellon, our faculty is pioneering the use of AI in mechanical engineering and preparing the next generation of engineers to do the same.
By partnering with Learning Engineers at CMU, our faculty have designed a practical, hands-on curriculum that covers the fundamental AI and machine learning techniques that engineers should know if they’re hoping to stay ahead in their field and make an immediate impact in their organization.
Curriculum Overview
After you enroll in the AI Engineering Fundamentals graduate certificate, you will take two graduate-level, credit-bearing courses. Each course will appear on your Carnegie Mellon transcript with the grade earned.
To earn the certificate, you must successfully complete both courses in the program. If you are only interested in one course, however, you may complete that course only and it will show on your transcript with the grade earned.
The certificate includes the following courses taught by CMU faculty:
Machine Learning & Artificial Intelligence for Engineers
Course Number: 24-887
Units: 12 units
Learn fundamental artificial intelligence and machine learning techniques for developing software that is foundational to next generation design and analysis tools. In this course, you will explore topics like supervised and unsupervised learning, feature engineering, model selection and optimization, dimensionality reduction, and ensemble learning, and then complete the course with an introduction to deep learning. You’ll not only learn the theory behind these techniques, but how to efficiently implement them as well.
Introduction to Deep Learning
Course Number: 24-888
Units: 12 units
Through hands-on activities, you will learn the foundations of deep neural networks, their applications to engineering tasks, and how to use deep learning to solve complex engineering problems. In this course, you will explore topics like convolutional neural networks, recurrent neural networks, long short-term memory, and generative adversarial networks.
Meet Our World-Class Faculty
Education: Ph.D., Carnegie Mellon University
Research Interests: Developing computational analysis, design, and manufacturing technologies that impact areas like mechanical CAD, topology optimization, additive manufacturing, and bio-engineering.
Education: Ph.D., University of Illinois at Urbana-Champaign
Research Interests: Applying and developing state-of-the-art machine learning algorithms to solve mechanical engineering problems, especially as they pertain to health and bio-engineering topics.
The Building Blocks of Our Curriculum
Real-World Focused
In this program, everything you learn serves a purpose—to help you solve real-world engineering problems. Throughout the coursework, you will practice solving problems in Jupyter Notebooks using least squares regression, support vector machines, decision trees, logistic regression, neural networks, clustering methods, dimensionality reduction techniques, ensemble learning techniques, and more. By the end, you will be able to describe and compare commonly used machine learning algorithms, explain their theoretical underpinnings, and use them to solve real-world engineering problems.
Hands-On Learning
As an engineer, you are a doer and a builder. In this program, you will learn core concepts by implementing various machine learning algorithms from scratch (for example, in Python) and by using industry-standard packages. You will also apply mathematical foundations for machine learning, including multivariate calculus, linear algebra, statistics, and optimization. When you complete the coursework, you will feel confident formulating data-driven approaches to AI engineering problems and communicating these solutions with algorithms and write-ups.
Practical Problem Solving
The field of AI for mechanical engineering can include some “out-there ideas”—but in this program, you’ll stay focused on what’s doable and relevant to today’s industries. Throughout the coursework, you will analyze practical engineering problems that will help you apply the machine learning concepts directly to your career, which will allow you to become more efficient, innovative, and successful in your approach and in the solutions you create.