数据科学基础（英文版）
￥76.00定价
作者： [美]阿夫里姆·布卢姆，[美]约翰·霍普克罗夫特
出版时间：2017-12
出版社：上海交通大学出版社
- 上海交通大学出版社
- 9787313182449
- 1版
- 99783
- 48257818-4
- 16开
- 2017-12
- 工学
- 计算机科学与技术
- TP274
- 理工类
- 本科
作者简介
内容简介
《数据科学基础（英文版）》是“大数据与计算机科学”系列教材之一，由国际著名计算机科学家约翰·霍普克罗夫特教授、阿夫里姆·布鲁姆教授和拉文德兰·坎南教授共同撰写。
《数据科学基础（英文版）》包含了高维空间、奇异值分解、随机行走和马尔可夫链、机器学习、大数据问题的算法、聚类随机图等主要内容。全书极大部分的结论都有严格的证明，且从第2章开始，每章后面均附有适量的练习题。
《数据科学基础（英文版）》可作为计算机及其相关专业本科生或研究生的教材，也可供专业技术人员参考。
《数据科学基础（英文版）》包含了高维空间、奇异值分解、随机行走和马尔可夫链、机器学习、大数据问题的算法、聚类随机图等主要内容。全书极大部分的结论都有严格的证明，且从第2章开始，每章后面均附有适量的练习题。
《数据科学基础（英文版）》可作为计算机及其相关专业本科生或研究生的教材，也可供专业技术人员参考。
目录
1 Introduction
2 High-Dimensional Space
2．1 Introduction
2．2 The Law of Large Numbers
2．3 The Geometry of High Dimensions
2．4 Properties of the Unit Ball
2．4．1 Volume of the Unit Ball
2．4．2 Volume Near the Equator
2．5 Generating Points Uniformly at Random from a Ball
2．6 Gaussians in High Dimension
2．7 Random Projection and Johnson-Lindenstrauss Lemma
2．8 Separating Gaussians
2．9 Fitting a Spherical Gaussian to Data
2．10 Bibliographic Notes
2．11 Exercises
3 Best-Fit Subspaces and Singular Value Decomposition （SVD）
3．1 Introduction
3．2 Preliminaries
3．3 Singular Vectors
3．4 Singular Value Decomposition （SVD）
3．5 Best Rank-k Approximations
3．6 Left Singular Vectors
3．7 Power Method for Singular Value Decomposition
3．8 Singular Vectors and Eigenvectors
3．9 Applications of Singular Value Decomposition
3．9．1 Centering Data
3．9．2 Principal Component Analysis
3．9．3 Clustering a Mixture of Spherical Gaussians
3．9．4 Ranking Documents and Web Pages
3．9．5 An Application of SVD to a Discrete Optimization Problem
3．10 Bibliographic Notes
3．11 Exercises
4 Random Walks and Markov Chains
4．1 Stationary Distribution
4．2 Markov Chain Monte Carlo
4．2．1 Metropolis-Hasting Algorithm
4．2．2 Gibbs Sampling
4．3 Areas and Volumes
4．4 Convergence of Random Walks on Undirected Graphs
4．5 Electrical Networks and Random Walks
4．6 Random Walks on Undirected Graphs with Unit Edge Weights
4．7 Random Walks in Euclidean Space
4．8 The Web as a Markov Chain
4．9 Bibliographic Notes
4．10 Exercises
5 Machine Learning
5．1 Introduction
5．2 Overfitting and Uniform Convergence
5．3 Illustrative Examples and Occam's Razor
5．3．1 Learning Disjunctions
5．3．2 Occam's Razor
5．3．3 Application： Learning Decision Trees
5．4 Regularization： Penalizing Complexity
5．5 Online Learning and the Perceptron Algorithm
……
6 Algorithms for Massive Data Problems： Streaming， Sketching， and Sampling
7 Clustering
8 Random Graphs
9 Topic Models， Non-Negative Matrix Factorization， Hidden Markov Models， and Graphical Models
10 Other Topics
11 Wavelets
12 Appendices
References
Index
2 High-Dimensional Space
2．1 Introduction
2．2 The Law of Large Numbers
2．3 The Geometry of High Dimensions
2．4 Properties of the Unit Ball
2．4．1 Volume of the Unit Ball
2．4．2 Volume Near the Equator
2．5 Generating Points Uniformly at Random from a Ball
2．6 Gaussians in High Dimension
2．7 Random Projection and Johnson-Lindenstrauss Lemma
2．8 Separating Gaussians
2．9 Fitting a Spherical Gaussian to Data
2．10 Bibliographic Notes
2．11 Exercises
3 Best-Fit Subspaces and Singular Value Decomposition （SVD）
3．1 Introduction
3．2 Preliminaries
3．3 Singular Vectors
3．4 Singular Value Decomposition （SVD）
3．5 Best Rank-k Approximations
3．6 Left Singular Vectors
3．7 Power Method for Singular Value Decomposition
3．8 Singular Vectors and Eigenvectors
3．9 Applications of Singular Value Decomposition
3．9．1 Centering Data
3．9．2 Principal Component Analysis
3．9．3 Clustering a Mixture of Spherical Gaussians
3．9．4 Ranking Documents and Web Pages
3．9．5 An Application of SVD to a Discrete Optimization Problem
3．10 Bibliographic Notes
3．11 Exercises
4 Random Walks and Markov Chains
4．1 Stationary Distribution
4．2 Markov Chain Monte Carlo
4．2．1 Metropolis-Hasting Algorithm
4．2．2 Gibbs Sampling
4．3 Areas and Volumes
4．4 Convergence of Random Walks on Undirected Graphs
4．5 Electrical Networks and Random Walks
4．6 Random Walks on Undirected Graphs with Unit Edge Weights
4．7 Random Walks in Euclidean Space
4．8 The Web as a Markov Chain
4．9 Bibliographic Notes
4．10 Exercises
5 Machine Learning
5．1 Introduction
5．2 Overfitting and Uniform Convergence
5．3 Illustrative Examples and Occam's Razor
5．3．1 Learning Disjunctions
5．3．2 Occam's Razor
5．3．3 Application： Learning Decision Trees
5．4 Regularization： Penalizing Complexity
5．5 Online Learning and the Perceptron Algorithm
……
6 Algorithms for Massive Data Problems： Streaming， Sketching， and Sampling
7 Clustering
8 Random Graphs
9 Topic Models， Non-Negative Matrix Factorization， Hidden Markov Models， and Graphical Models
10 Other Topics
11 Wavelets
12 Appendices
References
Index
