Mathematics for Machine Learning
- ISBN-10
- 1108569323
- ISBN-13
- 9781108569323
- Category
- Computers
- Language
- English
- Published
- 2020-04-23
- Publisher
- Cambridge University Press
- Authors
- Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Description
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.
Get the book
Other editions
Similar books
-
Mathematics for Machine Learning
By Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon OngEvery chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
-
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
By Jay DawaniThis book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms.
-
Math for Deep Learning: What You Need to Know to Understand Neural Networks
By Ronald T. KneuselMath for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.
-
Mathematics and Programming for Machine Learning with R: From the Ground Up
By William B. ClasterHighlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts ...
-
Data Science and Machine Learning: Mathematical and Statistical Methods
By Dirk P. Kroese, Thomas Taimre, Zdravko BotevThis book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code.
-
Machine Learning Math: A Complete Guide to Machine Learning for Beginners with Tensorflow. This Book Explains How to Build Artificial...
By ML and AI AcademyAre you looking for a complete guide of machine learning? Buy it NOW and let your customers get addicted to this awesome book!
-
Machine Learning: An Applied Mathematics Introduction
By Paul WilmottThis resource covers the essential mathematics behind all of the following topics: K Nearest Neighbours; K Means Clustering; Na-ve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural ...
-
Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence
By Richard HanThis book explains the math behind machine learning using simple but concrete examples.
-
Deep Learning
By Ian Goodfellow, Yoshua Bengio, Aaron CourvilleThe hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
-
Machine Learning with Spark - Second Edition
By Rajdeep Dua, Manpreet Singh Ghotra, Nick PentreathCreate scalable machine learning applications to power a modern data-driven business using Spark 2.xAbout This Book* Get to the grips with the latest version of Apache Spark* Utilize Spark's machine learning library to implement predictive ...