EAN: 9781108455145

MATHEMATICS FOR MACHINE LEARNING - MARC PETER D...

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.

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