Computer Science Theory for the Information Age covers the computer science theory likely to be useful in the next 40 years， including high- dimensional space， random graphs， singular value decomposition. random walks， Markov chains， learning algorithms， VC-dimension， algorithms for massive date problems， clustering. The book also covers graphical models and belief propagation， ranking and voting， sparse vectors， and compressed sensing. The book is intended for either an undergraduate or a graduate theory course in computer science. Prof. John Hopcroft is a world-renowned scientist and an expert on education in computer science. He was awarded the A. M. Turing Award in 1986 for his contributions in theoretical computing and data structure design. Dr. Ravindran Kannan is a principal researcher with Microsoft Research Labs located in India.

1 Introduction
2 High-Dimensional Space
2.1 Properties of High-Dimensional Space
2.2 The High-Dimensional Sphere
2.2.1 The Sphere and the Cube in Higher Dimensions
2.2.2 Volume and Surface Area of the Unit Sphere
2.2.3 The Volume is Near the Equator
2.2.4 The Volume is in a Narrow Annulus
2.2.5 The Surface Area is Near the Equator
2.3 Volumes of Other Solids
2.4 Generating Points Uniformly at Random on the Surface of a Sphere
2.5 Gaussians in High Dimension
2.6 Bounds on Tail Probability
2.7 Random Projection and the Johnson-Lindenstrauss Theorem
2.8 Bibliographic Notes
2.9 Exercises

3 Random Graphs
3.1 TheG（n， p） Model
3.1.1 Degree Distribution
3.1.2 Existence of Triangles in G （ n， d/n ）
3.2 Phase Transitions
3.3 The Giant Component
3.4 Branching Processes
3.5 Cycles and Full Connectivity
3.5.1 Emergence of Cycles
3.5.2 Full Connectivity
3.5.3 Threshold for O （Inn） Diameter
3.6 Phase Transitions for Monotone Properties
3.7 Phase Transitions for CNF-sat
3.8 Nonuniform and Growth Models of Random Graphs
3.8.1 Nonuniform Models
3.8.2 Giant Component in Random Graphs with Given Degree Distribution ...
3.9 Growth Models
3.9.1 Growth Model Without Preferential Attachment
3.9.2 A Growth Model with Preferential Attachment
3.10 Small World Graphs
3.11 Bibliographic Notes
3.12 Exercises

4 Singular Value Decomposition （SVD）
4.1 Singular Vectors
4.2 Singular Value Decomposition （SVD）
4.3 Best Rank k Approximations
4.4 Power Method for Computing the Singular Value Decomposition
4.5 Applications of Singular Value Decomposition
4.5.1 Principal Component Analysis
4.5.2 Clustering a Mixture of Spherical Gaussians
4.5.3 An Application of SVD to a Discrete Optimization Problem
4.5.4 Spectral Decomposition
4.5.5 Singular Vectors and Ranking Documents
4.6 Bibliographic Notes
4.7 Exercises

5 Random Walks and Markov Chains
5.1 Stationary Distribution
5.2 Electrical Networks and Random Walks
5.3 Random Walks on Undirected Graphs with Unit Edge Weights
5.4 Random Walks in Euclidean Space
5.5 The Web as a Markov Chain
5.6 Markov Chain Monte Carlo
5.6.1 Metropolis-Hasting Algorithm
5.6.2 Gibbs Sampling
5.7 Convergence of Random Walks on Undirected Graphs
5.7.1 Using Normalized Conductance to Prove Convergence
5.8 Bibliographic Notes
5.9 Exercises

6 Learning and VC-Dimension
6.1 Learning
6.2 Linear Separators， the Perceptron Algorithm， and Margins
6.3 Nonlinear Separators， Support Vector Machines， and Kernels
6.4 Strong and Weak Learning-Boosting
6.5 Number of Examples Needed for Prediction： VC-Dimension
6.6 Vapnik-Chervonenkis or VC-Dimension
6.6.1 Examples of Set Systems and Their VC-Dimension
6.6.2 The Shatter Function
6.6.3 Shatter Function for Set Systems of Bounded VC-Dimension
6.6.4 Intersection Systems
6.7 The VC Theorem
6.8 Bibliographic Notes
6.9 Exercises

7 Algorithms for Massive Data Problems
7.1 Frequency Moments of Data Streams
7.1.1 Number of Distinct Elements in a Data Stream
7.1.2 Counting the Number of Occurrences of a Given Element
7.1.3 Counting Frequent Elements
7.1.4 The Second Moment
7.2 Sketch of a Large Matrix
7.2.1 Matrix Multiplication Using Sampling
7.2.2 Approximating a Matrix with a Sample of Rows and Columns ...
7.3 Sketches of Documents
7.4 Exercises

8 Clustering
8.1 Some Clustering Examples
8.2 A Simple Greedy Algorithm for k-clustering
8.3 Lloyd's Algorithm for k-means Clustering
8.4 Meaningful Clustering via Singular Value Decomposition
8.5 Recursive Clustering Based on Sparse Cuts
8.6 Kernel Methods
8.7 Agglomerative Clustering
8.8 Communities， Dense Submatrices
8.9 Flow Methods
8.10 Linear Programming Formulation
8.11 Finding a Local Cluster Without Examining the Whole Graph
8.12 Axioms for Clustering
8.12.1 An Impossibility Result
8.12.2 A Satisfiable Set of Axioms
8.13 Exercises

9 Graphical Models and Belief Propagation
9.1 Bayesian or Belief Networks
9.2 Markov Random Fields
9.3 Factor Graphs
9.4 Tree Algorithms
9.5 Message Passing Algorithm
9.6 Graphs with a Single Cycle
9.7 Belief Update in Networks with a Single Loop
9.8 Maximum Weight Matching
9.9 Warning Propagation
9.10 Correlation Between Variables
9.11 Exercises

10 Other Topics
10.1 Rankings
10.2 Hare System for Voting
10.3 Compressed Sensing and Sparse Vectors
10.3.1 Unique Reconstruction of a Sparse Vector
10.3.2 The Exact Reconstruction Property
10.3.3 Restricted Isometry Property
10.4 Applications
10.4.1 Sparse Vector in Some Coordinate Basis
10.4.2 A Representation Cannot be Sparse in Both Time and Frequency Domains
10.4.3 Biological
10.4.4 Finding Overlapping Cliques or Communities
10.4.5 Low Rank Matrices
10.5 Exercises

11 Appendix
11.1 Asymptotic Notation
11.2 Useful Inequalities
11.3 Sums of Series
11.4 Probability
11.4.1 Sample Space， Events， Independence
11.4.2 Variance
11.4.3 Variance of Sum of Independent Random Variables
11.4.4 Covariance
11.4.5 The Central Limit Theorem
11.4.6 Median
11.4.7 Unbiased Estimators
11.4.8 Probability Distributions
11.4.9 Maximum Likelihood Estimation MLE
11.4.10 Tail Bounds
11.4.11 Chernoff Bounds： Bounding of Large Deviations
11.4.12 Hoeffding's Inequality
11.5 Generating Functions
11.5.1 Generating Functions for Sequences Defined by Recurrence Relationships
11.5.2 Exponential Generating Function
11.6 Eigenvalues and Eigenvectors
11.6.1 Eigenvalues and Eigenvectors
11.6.2 Symmetric Matrices
11.6.3 Extremal Properties of Eigenvalues
11.6.4 Eigenvalues of the Sum of Two Symmetric Matrices
11.6.5 Norms
11.6.6 Important Norms and Their Properties
11.6.7 Linear Algebra
11.6.8 Distance Between Subspaces
11.7 Miscellaneous
11.7.1 Variational Methods
11.7.2 Hash Functions
11.7.3 Catalan Numbers
11.7.4 Sperner's Lemma
11.8 Exercises
Index
References