Açıklama
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.
Baskılar ve ISBN
- 2019
- Cambridge University Press · İngilizce · 398 sayfa
ISBN 9781108569323 - 2020
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ISBN 9781108679930 - 2020
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Künye
- Yazarlar
- Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- İlk yayın
- 2019
- Yayınevi
- Cambridge University Press
- Sayfa (ortalama)
- 398
- Baskı sayısı
- 4
- Kaynak
- Open Library