Mathematics for Machine Learning

Mathematics for Machine Learning

 
Kiadó: Cambridge University Press
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GBP 37.99
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A termék adatai:

ISBN13:9781108455145
ISBN10:110845514X
Kötéstípus:Puhakötés
Terjedelem:398 oldal
Méret:252x177x18 mm
Súly:800 g
Nyelv:angol
Illusztrációk: 3 b/w illus. 106 colour illus.
1182
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Rövid leírás:

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Hosszú leírás:
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&&&160;and others&&&160;with a mathematical background, these derivations provide a starting point to machine learning texts. For&&&160;those&&&160;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.

'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal
Tartalomjegyzék:
1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.