How Electrical Engineering and Computer Engineering Departments are Preparing Undergraduate Students for the New Big Data, Machine Learning, and AI Paradigm: A Three- Model Overview

Abstract

Big data, machine learning, and artificial intelligence (Big Data ML-AI) applications are revolutionizing the methods and practices of electrical and computer engineering (ECE), from autonomous vehicles and futuristic semi-conductor design to smart phones and speakers. But is the undergraduate ECE curriculum keeping pace with the new Big Data ML-AI paradigm? This exploratory study is designed to investigate whether or not, and how ECE departments are adapting the first two years of their curriculum, so as to sustainably integrate Big Data ML-AI courses and content. The research questions are: Which Big Data ML-AI tools are being used in industry, and are these tools reflected in the ECE curriculum? To answer these questions, the datasets consist of 2018 surveys of Big Data ML-AI tools that are being utilized in industry, and a review of the undergraduate ECE syllabi of five selected universities from the group of American Association of Universities. The results show that Python, Tensorflow, and Keras are the most utilized Big Data ML-AI tools, while only one of the five selected institutions has fully adapted the first two years of the undergraduate ECE curriculum, so as to flexible integrate Big Data ML-AI courses and concepts. Additionally, objections to the integration of Big Data ML-AI into the ECE curriculum, such as technology transience and the detrimental nature of biased algorithms, are addressed.