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
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##相较而言我更喜欢前半部分有关于数学基础的部分，深入浅出。
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
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##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##差不多是见人就吹了
评分##粗略翻了一下，开始ml之前复习一下数学基础。。
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##市面上最好的机器学习入门教材（我菜我先说）
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