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M.Z. Naser, PhD, PE

Exciting news! I am thrilled to announce the launch of my new textbook, 
 

Machine Learning for Civil & Environmental Engineers: A Practical Approach to Data-driven Analysis, Explainability, and Causality

 

Links to this book [Wiley] [Amazon] [Barnes & Noble] [Table of content] [ISBN: 978-11198976061]

During the 2021 ASCE convention, I was approached by Wiley with this project. The timing could not have been better as I was also working on an ML-based course at the same time. As you know, there are so many ways to write a book! Evidently, I decided to write this textbook from the perspective of an engineer while keeping in mind students and engineers with little, to no coding or ML experience. I might be a lil biased, but I believe it can serve as a “great” textbook for teaching ML as a methodology and philosophy.

This textbook delves into how engineers can leverage ML to develop a new generation of models to overcome the ever-growing and challenging problems we continue to face. It starts with basic data-driven (blackbox) and physics-informed analysis and then dives into explainable (whitebox) and causal ML. The book passes by statistics and other traditional methods inherent to CEEs and shows both sides of ML (success stories vs. poor examples). This is going to be an exciting journey!

In this textbook, you will:

  • Understand, visualize, and apply various (25+) ML algorithms to different civil and environmental engineering problems (e.g., those belonging to tabular and computer vision (deep learning) and supervised and unsupervised learning)

  • Practice many, many, many practical examples, datasets, code scripts, and tutorials through coding-free and coding-based (Python and R) platforms. These examples cover topics spanning structural, transportation, fire, and environmental engineering, as well as construction materials and management (thanks to my students Moe al-Bashiti and Arash Tapeh for their help!)

  • Access a full set of lecture notes (ppts), homework problems, projects, study plans, and exams with solutions. I will be very happy to chat with interested faculty and lecturers on adoption, delivery, and presentation strategies😊

 

Whether you are a student, engineer, faculty, or ML enthusiast, please feel free to share your thoughts with me!

Things I can promise you:

  • This is not another “learn ML by coding” textbook

  • This book is written in very simple and straight-to-the-point language (with a couple of easter eggs from pop culture (and beyond)).

  • No Chat-GPT-like tools were involved! (maybe next time 😊)

  • A more formal announcement will be made once the artwork and book are out with a complete list of thank yous for the many individuals that made this project see the light!

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