Download Instructor Resources of Mathematics for Machine Learning - Instructor Resources (Instructor Manual + Solutions for Jupyter Notebook Tutorials + Additional Exercises)
PDF Please note that by purchasing this product, you will only receive instructor resources related to the book. This product does not include the book itself. by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Information
Format: PDF Language: English Publisher: Cambridge University Press Publication Date of the Electronic Edition: 2020
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ISBN: 9781108470049, 1108470041, 9781108569323, 1108569323
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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 studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning 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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Delivery time: Instant