Mathematics for Machine Learning
Marc Peter Deisenroth
Reading Time
at 250 WPM6h 38m
The average reader, reading at a speed of 250 WPM, would take 6h 38m to read Mathematics for Machine Learning.
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14
days at 30 min/day
398
total minutes
Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Published
2019
Publisher
Cambridge University Press
Pages
398
ISBN-13
9781108569323
Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Subjects
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Deep Learning
Artificial Intelligence and Intelligent Systems
Introduction to Machine Learning with Python
Machine learning
The Master Algorithm
Frequently Asked Questions
How many pages are in Mathematics for Machine Learning?
This edition of Mathematics for Machine Learning has approximately 398 pages. Please note, this is an estimate and the exact page count can vary between hardcover, paperback, and e-book versions.
How long does it take to read Mathematics for Machine Learning?
For most readers, Mathematics for Machine Learning typically takes between 8h 18m and 5h 32m to complete. This is based on the book's length of approximately 99,500 words and common reading speeds.
Here's a detailed breakdown: • Continuous reading at 250 WPM: approximately 6h 38m of focused reading • Casual reading (30 minutes/day): you could finish in roughly 14 days • Estimated word count: 99,500 words
Your individual reading time will vary based on your personal reading pace, the amount of daily reading time, and your familiarity with the subject matter.
What is the word count of Mathematics for Machine Learning?
The estimated word count for Mathematics for Machine Learning is approximately 99,500 words. This figure is calculated using industry-standard methods that consider genre-specific word density patterns, typical formatting and layout characteristics, and standard words-per-page ratios for published books.
This is an approximation — actual word count may vary based on font size, formatting, edition, and the presence of illustrations or charts.
Who is the author of Mathematics for Machine Learning?
Mathematics for Machine Learning was written by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
When was Mathematics for Machine Learning published?
The publication date for this specific edition is 2019. The original work may have been published on a different date.