Machine Learning for Science and Engineering

Volume I: Fundamentals
Teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply machine learning (ML) to real-world problems. The books builds the mathematical pillars required to comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science.
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. This textbook 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 thoroughly comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science. Exercises with raw field data, including well logs and weather measurements, prepare and encourage the reader to begin using software to validate results and program their own creative data solutions. Most importantly, the reader always keeps an eye on ML's imperfect data situations as encountered in the real world.

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