3 New AI and ML Courses Offered in the Woodruff School
August 7, 2025
By Mikey Fuller
Three new or modified courses have been developed by George W. Woodruff School of Mechanical Engineering faculty members to enhance student preparedness for leveraging the growing relevance of artificial intelligence (AI) and machine learning (ML) technology in mechanical engineering and nuclear and radiological engineering fields.
“These courses were developed in response to the interest of our own students in these disciplines, and the growing need from our industry partners for engineers graduating with AI-fluency,” Associate Chair for Undergraduate Studies and Woodruff Professor Brandon Dixon said.
In addition, the courses can be used towards the applications of AI and ML minor that was launched in the 2024-2025 academic year as a collaborative effort between the College of Engineering and the Ivan Allen College of Liberal Arts.
This interdisciplinary minor equips students with the skills and knowledge to use AI and ML to solve problems in engineering, humanities, and social sciences. The curriculum is also designed to provide students with the insight to describe and discuss current ethics and policy frameworks related to AI and ML.
These new or reintroduced courses in the curriculum will serve as the backbone for AI and ML training within Georgia Tech and the Woodruff School:
- ME/MSE 4710 – Foundations in Machine Learning for Engineers
- ME 4853-JAJ – AI for Design and Manufacturing
- ME 4853-WAN – Machine Learning Methods for Mechanical Engineering
Funded by the College of Engineering and the Woodruff School, the curriculum was designed by Amit Jariwala, director of design, innovation and experiential learning; Associate Professor Roger Jiao; Surya Kalidindi, Regents' Professor and Rae S. and Frank H. Neely Chair; Aaron Stebner, Eugene C. Gwaltney, Jr. Chair in Manufacturing and professor; and Professor Yan Wang.
ME/MSE 4710 – Foundations in Machine Learning for Engineers, taught by Stebner, will introduce students to the fundamentals of data science, emphasizing the collection, categorization, and quantification of raw data. Students will learn the mathematical skills and foundational principles of statistics and data analytics needed to critically evaluate ML implementations and identify when, why, and how to use ML to solve engineering problems.
Jariwala and Jiao will teach ME 4853-JAJ – AI for Design and Manufacturing. This course will guide students to structure and interpret information into actionable knowledge. Students will learn fundamental AI methods and intelligent decision support techniques for creating, analyzing, synthesizing, and implementing design solutions to open-ended problems in manufacturing system planning and operations optimization. It is designed to help students bridge the gap between the creative decision-making in design they learned in ME 2110, and the practical application of engineering concepts they learned in Capstone Design in ME 4182.
ME 4853-WAN – Machine Learning Methods for Mechanical Engineering, taught by Kalidindi and Wang, was first offered in Spring 2025 and will be offered again in Spring 2026.
This course teaches machine learning techniques that contextualize and connect data to extract meaningful information. It introduces students to fundamental methods and models of AI and ML by giving them hands-on experience using the latest ML software tools to solve mechanical engineering problems through lab sessions and course projects.
Jiao believes these three courses form a well-aligned educational pathway anchored in the foundational AI principle of transforming data into information, and information into knowledge.
“This coherent curriculum equips students with cutting-edge skills and a deep understanding of AI’s role in mechanical engineering, empowering them to innovate and lead in their professional careers,” Jiao said.
