Seismic Soundoff

As the size and complexity of data soars exponentially, machine learning (ML) has gained prominence in applications in geoscience and related fields. ML-powered technology increasingly rivals or surpasses human performance and fuels a large range of leading-edge research. In this conversation with host Andrew Geary, mathematician Herman Jaramillo discusses his new book, Machine Learning for Science and Engineering Volume One: Fundamentals. This book teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply ML to real-world problems. It builds the mathematical pillars required to comprehend and master modern ML concepts thoroughly and translates the newly gained mathematical understanding into better-applied data science. Herman explains why this book is a unique contribution to the growing field of machine learning, the role of intuition in using ML, and what's in this book that you rarely find in other ML books. He also goes in-depth on the critical understandin