This groundbreaking book transcends traditional machine learning
approaches by introducing information measurement methodologies
that revolutionize the field. Stemming from a UC Berkeley seminar
on experimental design for machine learning tasks, these techniques
aim to overcome the 'black box' approach of machine learning by
reducing conjectures such as magic numbers (hyper-parameters) or
model-type bias. Information-based machine learning enables data
quality measurements, a priori task complexity estimations, and
reproducible design of data science experiments. The benefits
include significant size reduction, increased explainability, and
enhanced resilience of models, all contributing to advancing the
discipline's robustness and credibility. While bridging the gap
between machine learning and disciplines such as physics,
information theory, and computer engineering, this textbook
maintains an accessible and comprehensive style, making complex
topics digestible for a broad readership. Information-Driven
Machine Learning explores the synergistic harmony among these
disciplines to enhance our understanding of data science modeling.
Instead of solely focusing on the "how," this text provides answers
to the "why" questions that permeate the field, shedding light on
the underlying principles of machine learning processes and their
practical implications. By advocating for systematic methodologies
grounded in fundamental principles, this book challenges industry
practices that have often evolved from ideologic or profit-driven
motivations. It addresses a range of topics, including deep
learning, data drift, and MLOps, using fundamental principles such
as entropy, capacity, and high dimensionality. Ideal for both
academia and industry professionals, this textbook serves as a
valuable tool for those seeking to deepen their understanding of
data science as an engineering discipline. Its thought-provoking
content stimulates intellectual curiosity and caters to readers who
desire more than just code or ready-made formulas. The text invites
readers to explore beyond conventional viewpoints, offering an
alternative perspective that promotes a big-picture view for
integrating theory with practice. Suitable for upper undergraduate
or graduate-level courses, this book can also benefit practicing
engineers and scientists in various disciplines by enhancing their
understanding of modeling and improving data measurement
effectively.
General
Imprint: |
Springer International Publishing AG
|
Country of origin: |
Switzerland |
Release date: |
December 2023 |
First published: |
2023 |
Authors: |
Gerald Friedland
|
Dimensions: |
235 x 155mm (L x W) |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-139476-8 |
Categories: |
Books
|
LSN: |
3-03-139476-3 |
Barcode: |
9783031394768 |
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