Description
Mathematics for Machine Learning is a comprehensive book authored by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press in 2020. The book serves as a foundational resource for individuals interested in machine learning, focusing on the mathematical principles that underpin the field. It is divided into two parts: Mathematical Foundations and Central Machine Learning Problems.
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The first part covers essential topics such as linear algebra, analytic geometry, matrix decompositions, vector calculus, probability and distribution, and continuous optimization. These topics provide the necessary groundwork for understanding machine learning algorithms.
The second part of the book delves into specific machine learning problems, including linear regression, dimensionality reduction with principal component analysis, density estimation with Gaussian mixture models, and classification with support vector machines. The book is designed to be concise, offering a clear and accessible introduction to the mathematical concepts required for machine learning.
Mathematics for Machine Learning is available in PDF format, with additional resources such as tutorials and exercises provided to enhance learning. The book is highly recommended by experts in the field, including Joelle Pineau from McGill University and Facebook, Christopher Bishop from Microsoft Research Cambridge, and Pieter Abbeel from the University of California, Berkeley. It is suitable for newcomers to machine learning as well as researchers and engineers seeking a reference text.
- CategoryEducation & Research
Book Details
Mathematical foundations for machine learning
Linear algebra and analytic geometry
Matrix decompositions and vector calculus
Probability and distribution
Continuous optimization techniques
Machine learning algorithms explained
Linear regression and PCA
Gaussian mixture models and SVM
Who This Book Is For
- Educational resource
- Reference text
- Skill development
- Tutorials
- Algorithm understanding
