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Mathematics for machine learning /

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 compute...

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Bibliographic Details
Main Authors: Deisenroth, Marc Peter (Author), Faisal, A. Aldo (Author), Ong, Cheng Soon (Author)
Format: Book
Language:English
Published: Cambridge ; New York : Cambridge University Press, [2020]
Subjects:
Machine learning > Mathematics.
Table of Contents:
  • Introduction and motivation
  • Linear algebra
  • Analytic geometry
  • Matrix decompositions
  • Vector calculus
  • Probability and distribution
  • Continuous optimization
  • When models meet data
  • Linear regression
  • Dimensionality reduction with principal component analysis
  • Density estimation with Gaussian mixture models
  • Classification with support vector machines.