Machine Learning for Civil and Environmental Engineers

1 Teaching Methods for This Textbook

Deciding is for machines because its computational. Choosing is very human.

Joseph Weizenbaum

Synopsis

This brief chapter outlines some proposed suggestions for adopting this textbook into a civil and environmental engineering curriculum. I try to remain flexible with describing my suggestions¹ as I understand the complexity of integrating new courses and/or modules into a dense curriculum – just like ours. I hope these can be of aid.²

1.1 Education in Civil and Environmental Engineering

There is a rich body of literature that tackles a variety of teaching and learning methods targeting engineering students [1–3]. If we are to distill this literature into its fundamental facets, we arrive at the elemental conclusion that many engineering students can be described as active learners. Such learners are likely to be experimentalists and tend to value cause-and-effect demonstrations. This presents an exciting challenge to drafting this book – and to be quite honest, it remains a challenge that I try to overcome every time I present ML to a new or junior audience. Before jumping into some possible means of delivery, please allow me to walk you through the following.

Foundational methods within our domain are developed systematically and with methodical consistency. In the majority of the time, such methods have a clear approach. We start from the first principles and work our way up to a goal (i.e., a solution). Suppose we think of designing ...