Putting the "Machine" Back in Machine Learning for Engineering Students

Rudy Chin, Dimitrios Stamoulis, Diana Marculescu

Abstract: Computer hardware architecture has played an important role in the recent advances made in deep learning and associated applications. However, effective teaching strategies for hardware architectures for machine learning require a different structure and technical background than classic machine learning. More specifically, not only does the material need to convey necessary machine learning concepts to students, but also covers the hardware and software infrastructure concepts required for supporting machine learning systems. In this paper, we describe our approach to designing the course materials along with student assessment and evaluation for the ``Hardware Architectures for Machine Learning" course targeting Electrical and Computer Engineering graduate students.