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
##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##过浅, 只适合速览
评分##读完了。很难说这是本machine learning数学入门书。因为每个人接触machine learning的目的千差万别，从从事算法研究到从事其他行业想通过一些工具对自己的数据获得更多insight的。所以对数学的要求也千差万别，以norm这个概念为例，有些人需要理解满足对称性，正定性，三角不等式的方程都是norm，而另外一些人了解norm是长度就足够了。回头看，我觉得自己作为一个打工人，不是很需要这本书，当然不是数学不重要，只是把时间花在更工程师向的书里性价比会更高一点。
评分读了数学基础部分，内容不多，但是把一些简单的概念讲得更加透彻，有助于建立数学思维体系
评分读了数学基础部分，内容不多，但是把一些简单的概念讲得更加透彻，有助于建立数学思维体系
评分##过浅, 只适合速览
评分##开源好评
评分##写的不错,难度适中
评分##不管是拿来入门还是重温都很适合
相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2026 book.teaonline.club All Rights Reserved. 图书大百科 版权所有