Mathematical principles continue to revolutionize and expand the application domains related to artificial neural networks (ANNs), a subfield of machine learning (ML). How should deep learning, a form of ANNs, be effectively incorporated into the information technology (IT) curriculum for undergraduate students without a strong mathematics background? Recognizing the existing incorporation of deep learning into areas such as the creative arts and music, Hoover et al. present course topics, software tools, and lab assignments and projects for effectively infusing deep learning into an IT curriculum.
The authors succinctly review numerous applications of ML, ANN, and deep learning in the literature. The result is an exemplary model for instilling deep learning into the entire IT curriculum. They outline the learning outcomes and computational tools used to successfully incorporate deep learning skills into an IT course. The deep learning components of an IT curriculum include the generalized architectures that use ANNs to solve network traffic problems originating from different packet routing protocols going through the layers of interconnected networks.
The authors’ experiments encourage students to use the deep learning tools available in Python libraries to creatively solve IT problems. Laboratory assignments and projects, examinations, and presentations are used to ascertain the extent to which students learned how to use tools to solve deep learning problems. The experimental results reveal that student performance improved during the deep learning training period. Given the diverse nature of students engaged in this research, future studies should perhaps further investigate the various challenges encountered.
All educational professionals should consider how to revamp current IT curriculum models by incorporating more deep learning, ANN, and ML concepts, fundamentals, and applications.