Mathematics for Machine Learning

Cambridge University Press978-1-108-47004-9 Mathematics for Machine LearningMarc Peter Deisenroth , A. Aldo Faisal , Cheng Soon Ong FrontmatterMore in this web service Cambridge University PressMathematics for Machine LearningThe fundamental mathematical tools needed to understand Machine Learning includelinear algebra, analytic geometry, matrix decompositions, vector calculus, optimiza-tion, probability, and statistics. These topics are traditionally taught in disparatecourses, making it hard for data science or computer science students, or profes-sionals, to efficiently learn the self-contained textbook bridges the gap between mathematical and machinelearning texts, introducing the mathematical concepts with a minimum of prerequi-sites.

extending statistical machine learning methods. He received his PhD in computer ... 8.4 Probabilistic Modeling and Inference 244 8.5 Directed Graphical Models 249 8.6 Model Selection 254 ... 12.2 Primal Support Vector Machine 338 12.3 Dual Support Vector Machine 347 12.4 Kernels 351 12.5 Numerical Solution 353

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  Machine, Statistical, Mathematics, Learning, Inference, Statistical machine learning, Mathematics for machine learning

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