Tech debuts AI-centered engineering courses


As artificial intelligence (AI) and machine learning (ML) become increasingly common in daily life, the Institute has taken measures to adapt to this changing landscape of technology. While these topics have been a stronghold of computer science disciplines at Tech, the College of Engineering (COE) has recently made efforts to integrate them into their programs. 

While often used interchangeably, AI and ML diverge in some respects. ML involves using algorithms to recognize patterns and gradually learn from data, and AI refers to training machines to mimic and exceed human behaviors. AI is a broader and developing field, while ML has been more established and is one of the pathways for creating AI.

Beginning in Fall 2023, the COE added or updated six courses across its schools to include these interrelated concepts. 

Two of the courses continued into the Spring semester, and six more debuted with updated curriculums. 

These classes span six of the eight departments within the college, and more courses may arise in future semesters.

While the departments begin to integrate AI and ML into their engineering curriculums, these concepts have been present in computer science programs for years. Specifically, undergraduate CS students can follow the Intelligence thread to gain experience with AI and ML throughout their time at the Institute; however, engineering students will tend to take these courses later in their college careers. 

“[Computer science has] been very good at owning it, maybe faster than others, but if you look at engineering, machine learning is everywhere,” said Matthieu Bloch, ECE professor and Associate Dean for Academic Affairs in the COE. He added that core facets of AI, like optimization and linear algebra, are already present in engineering curricula. 

The integration of these subjects into the COE reflects the growing presence of computer science disciplines at Tech, as undergraduate enrollment in the College of Computing (COC) has tripled in the past decade while the number of undergraduate engineers has slightly decreased in the same period. 

Harnessing the strengths of both Colleges, the new AI and ML courses will allow engineering graduates to thrive in a world increasingly reliant upon data and its analysis. 

Bloch contributed to the push for including AI and ML in courses from recent industry interest in the topics, student demands for relevant classes and the emergence of these analysis tools in all engineering fields. Additionally, Bloch believes the new classes and AI minor will help with “creating opportunities” and will allow students to be “recognized for interests that they have” in these emerging fields.

The Institute itself has also taken steps to integrate AI and ML across disciplines, with the recently announced AI Hub as an intersectional space for these innovations.

Larry Heck, ECE and ISyE professor and interim Co-Executive Director of the AI Hub, said that the space serves to “pull together AI researchers from all across the Institute” and “support the interdisciplinary connection of AI efforts and the needs.” 

The AI Hub helps to compute, provide data and facilitate industry sponsorship for the Institute, further cementing its status as a hotspot for AI and ML innovation. 

Additionally, the team is working to “apply those AI technologies back to Georgia Tech, making an AI-power university,” which could use its advancements to provide teaching tools or other help to faculty and students. 

While engineering students will have more opportunities to learn about AI and ML during their time at Tech, these new courses engage faculty in shaping the direction of engineering education. 

“The goal is to introduce students to how algorithms, statistics and machine learning are used to analyze biological data,” said Saurabh Sinha, ISyE and BMED professor, about his Introduction to Bioinformatics course that is cross-listed in both departments. Speaking on the importance of teaching AI concepts in biomedical engineering, Sinha explained that “the future is going to be heavily reliant on data.”

He continued that AI and ML have been present in the discipline for years, and it is natural to educate students on them as “the state of the art in data analysis is machine learning,” implying future engineers will need experience with these innovative tools. Sinha hopes his students will embrace AI and ML in earnest as there are numerous applications for the concepts already and more will surely rise in the future.

Although the AI and ML courses are scattered across the College, many are excited to see how these topics will be incorporated into engineering curricula, as industry and academic applications of these tools grow. 

Pulling on existing resources, especially from the COC, the Institute hopes to make significant strides in the fields as it tries to best prepare its students for their bright futures that interface with modern industry developments. 



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