Mathematics for Machine Learning

I. THÔNG TIN SẢN PHẨM

Mã sản phẩm    : STT164
Nhà xuất bản: Cambridge University Press; 1st edition (April 23, 2020)
Số trang    : 416 trang
Tác giả        : Marc Peter Deisenroth
Ngôn Ngữ    : Tiếng Anh
ISBN : 110845514X
Hình thức    : Bìa Mềm, IN ĐEN TRẮNG
Loại         : Sách gia công
Giấy in        : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng    : Bản in rõ nét, giá rất tốt cho mọi người.

II. MÔ TẢ SẢN PHẨM

1) Mô tả sản phẩm
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.

2) Tác giả
Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London. Prior to this, he was a faculty member in the Department of Computing, Imperial College London. His research areas include data-efficient learning, probabilistic modeling, and autonomous decision making.