Episode 189: How to apply machine learning to real-world problems

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 understanding of finding the best-suited algorithm. This conversation and book explore the hottest topics facing students, scientists, and engineers. And this episode will provide a solid foundation to understand how to utilize this cutting-edge science in your work.

Dr. Herman Jaramillo teaches at the University of Medellín and is a member of the Research Group on Scientific Modeling and Computing.

Seismic Soundoff · 189: How to apply machine learning to real-world problems

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Seismic Soundoff explores the depth and usefulness of geophysics for the scientific community and the public. If you want to be the first to know about the next episode, please follow or subscribe to the podcast wherever you listen to podcasts. Two of our favorites are Apple Podcasts and Spotify.

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Zach Bridges created original music for this show. Andrew Geary hosted, edited, and produced this episode at TreasureMint. The SEG podcast team is Jennifer Cobb, Kathy Gamble, and Ally McGinnis. 

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