AI education matters: a first introduction to modeling and learning using the data science workflow

Traditionally artificial intelligence (AI) and machine learning (ML) courses are taught at the senior and graduate level in higher-education computer science curricula following the mastery learning strategy, cf. Figure 1. This makes sense, since most AI and ML models and the theory behind them require a substantial understanding of probability and statistics, as well as advanced calculus and matrix algebra. To understand Logistic Regression as a probabilistic classifier performing maximum-likelihood or maximum-a-posteriori estimation, for example, students need to understand joint and conditional probability distributions. In order to derive the back propagation algorithm to train Neural Networks students need to understand partial derivatives and inner and outer tensor products. These are just two of many examples where substantial mathematical background - typically taught at the junior level in a computer science major program - is required. With AI and ML algorithms being used more widely by enterprises across domains, as well as, in applications and services we use in our daily lives, it makes sense to raise awareness about what AI is, what it can and cannot do, and how it is used to solve problems to a broader audience. Very much in the same spirit as the "CS for all" idea (https://www.csforall.org), we have to extend our curricula to include introductory courses to AI and ML on the early undergraduate level (or even in high-school) to expose students to the ideas and working principles of AI technology. One way to achieve this is to introduce the principles of working with data, modeling, and learning through the data science workflow.