How to break into machine learning

4 minute read

Published: October 24, 2022

I have a master’s degree in civil and structural engineering, where I dedicated the last year of my MSc study, including my MSc thesis, to vibration-based damage detection using machine learning (ML). At that time, I was working together with the Monitoring of Structures research group at Aarhus University, who wanted to apply their structural health monitoring technologies within a probabilistic framework to perform damage detection. I had a solid background in applied mathematics and a semi-solid background in classical statistics, but machine learning was a completely new field to me. As most people would do, I stated out by finding book recommendations on the topic, collected the most highly rated books, i.e., Bishop (2006) and Murphy (2012), and dived in. These books are somewhat of a mouthful when you are not familiar with the (Bayesian) ML jargon and workflow, but then I found Andrew Ng’s original, online Stanford/Coursera.org course, which became a game changer for me, and I now have a PhD in probabilistic modeling and analysis (ML) and work as a Postdoc and consultant in this field.