Description: Mathematics for Machine Learning Paperback by Marc Peter Deisenroth PRODUCT 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. © Copyright . All rights reserved.
Price: 63.99 USD
Location: Chelmsford
End Time: 2024-12-21T15:26:13.000Z
Shipping Cost: 4.99 USD
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Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
MPN: 9781108455145
Number of Pages: 398 Pages
Publication Name: Mathematics for Machine Learning
Language: English
Publisher: Cambridge University Press
Subject: General, Computer Vision & Pattern Recognition
Item Height: 0.7 in
Publication Year: 2020
Type: Textbook
Item Weight: 28.2 Oz
Author: Cheng Soon Ong, A. Aldo Faisal, Marc Peter Deisenroth
Subject Area: Computers, Science
Item Length: 9.9 in
Item Width: 7 in
Format: Trade Paperback
