Introduction and Motivation
Linear Algebra
Analytic Geometry
Matrix Decompositions
Vector Calculus
Probability and Distribution
Continuous Optimization
Part II: Central Machine Learning Problems
When Models Meet Data
Linear Regression
Dimensionality Reduction with Principal Component Analysis
Density Estimation with Gaussian Mixture Models
Classification with Support Vector Machines
· · · · · · (收起)
具体描述
https://mml-book.github.io/
::This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics::
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.
用户评价
##不管是拿来入门还是重温都很适合
评分##特别适合像我这种已经n年没学过数学的人，也很适合做reference有什么不懂的时候即兴翻翻
评分##虽然很基础，但是对于有些东西经常会给出多种角度的解释，总有一种能让人容易理解和接受，还不错的书，但是如果花太长时间看就比较不值得
评分##不管是拿来入门还是重温都很适合
评分##不管是拿来入门还是重温都很适合
评分##很不错，就是最复杂的算法到svm，第二部分再多一些算法就更好了
评分##过浅, 只适合速览
评分##不管是拿来入门还是重温都很适合
评分##只读了第一部分的数学基础，快速地过了一遍，还挺不错的
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