Cutting-Edge Curriculum
Preparing engineers for an industry shift to AI
The engineering industry is experiencing a monumental shift toward artificial intelligence. At Carnegie Mellon, our faculty is pioneering the use of AI in engineering and preparing the next generation of engineers to do the same.
By partnering with Learning Engineers, our faculty have designed an effective online learning environment where engineers can study, apply and implement the AI and machine learning techniques they will need to enhance their skill set, remain competitive in their industry, and make an immediate impact in their organization.
Curriculum Overview
Carnegie Mellon currently offers two credit-bearing, graduate-level certificates in the field of AI Engineering. The first certificate (AI Engineering Fundamentals) is a prerequisite for the second certificate and features the following course progression:
Individuals who meet the admission requirements are encouraged to apply for the first certificate in AI Engineering Fundamentals. After mastering foundational topics in this certificate and successfully completing the coursework, these individuals may choose to explore more complex topics in the second certificate called Advanced AI Models for Engineering. A curriculum overview for each certificate can be found below.
Graduate Certificate in AI Engineering Fundamentals
Graduate Certificate in Advanced AI Models for Engineering*
Meet Our World-Class Faculty
Dr. Levent Burak Kara
Professor of Mechanical Engineering
Education: Ph.D., Carnegie Mellon University
Research Focus: Developing new computational analysis, design, and manufacturing technologies with wide-ranging applications in areas like mechanical CAD, topology optimization, additive manufacturing, electronics design, and bio-engineering. In his research, Dr. Kara combines principles of machine learning, optimization, and geometric modeling to develop new knowledge and computational software for use in next-generation design systems.
Research Lab: Visual Design and Engineering Lab
Dr. Amir Barati Farimani
Associate Professor of Mechanical Engineering
Education: Ph.D., University of Illinois at Urbana-Champaign
Research Focus: Applying machine learning, data science, and molecular dynamics simulations to health and bio-engineering problems. Dr. Farimani’s lab unites researchers with different backgrounds (including physics, materials science, mechanical engineering, bio-engineering, chemical engineering, and computer science) to bring the state-of-the-art machine learning algorithm to mechanical engineering.
Research Lab: Mechanical and AI Lab (MAIL)
The Graduate Certificate in AI Engineering Fundamentals and the Graduate Certificate in Advanced AI Models for Engineering are offered by the Department of Mechanical Engineering (MechE), which is housed within CMU’s highly-ranked College of Engineering. MechE faculty members are highly distinguished in their field and many of them are currently collaborating on high-profile projects with AI and machine learning technology. Check out some of their work below.

Applying ML techniques to automate the design process of electronic circuits and chips
Using AI to design a better method for desalination and provide the world with drinking water
Comparing the effectiveness of human versus human-AI teams in the context of engineering
Using deep learning to research material transport in the brain, which could improve our understanding of disease development
The Building Blocks of Our Curriculum
Real-World Focused
In these programs, 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 these programs, 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 engineering can include some “out-there ideas”—but in these programs, 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.