Mathematical Foundation for Data Analysis

Preface
Acknowledgements
Contents
1 Probability Review
	1.1 Sample Spaces
	1.2 Conditional Probability and Independence
	1.3 Density Functions
	1.4 Expected Value
	1.5 Variance
	1.6 Joint, Marginal, and Conditional Distributions
	1.7 Bayes' Rule
		1.7.1 Model Given Data
	1.8 Bayesian Inference
	Exercises
2 Convergence and Sampling
	2.1 Sampling and Estimation
	2.2 Probably Approximately Correct (PAC)
	2.3 Concentration of Measure
		2.3.1 Markov Inequality
		2.3.2 Chebyshev Inequality
		2.3.3 Chernoff-Hoeffding Inequality
		2.3.4 Union Bound and Examples
	2.4 Importance Sampling
		2.4.1 Sampling Without Replacement with Priority Sampling
	Exercises
3 Linear Algebra Review
	3.1 Vectors and Matrices
	3.2 Addition and Multiplication
	3.3 Norms
	3.4 Linear Independence
	3.5 Rank
	3.6 Square Matrices and Properties
	3.7 Orthogonality
	Exercises
4 Distances and Nearest Neighbors
	4.1 Metrics
	4.2 Lp Distances and their Relatives
		4.2.1 Lp Distances
		4.2.2 Mahalanobis Distance
		4.2.3 Cosine and Angular Distance
		4.2.4 KL Divergence
	4.3 Distances for Sets and Strings
		4.3.1 Jaccard Distance
		4.3.2 Edit Distance
	4.4 Modeling Text with Distances
		4.4.1 Bag-of-Words Vectors
		4.4.2 k-Grams
	4.5 Similarities
		4.5.1 Set Similarities
		4.5.2 Normed Similarities
		4.5.3 Normed Similarities between Sets
	4.6 Locality Sensitive Hashing
		4.6.1 Properties of Locality Sensitive Hashing
		4.6.2 Prototypical Tasks for LSH
		4.6.3 Banding to Amplify LSH
		4.6.4 LSH for Angular Distance
		4.6.5 LSH for Euclidean Distance
		4.6.6 Min Hashing as LSH for Jaccard Distance
	Exercises
5 Linear Regression
	5.1 Simple Linear Regression
	5.2 Linear Regression with Multiple Explanatory Variables
	5.3 Polynomial Regression
	5.4 Cross-Validation
		5.4.1 Other ways to Evaluate Linear Regression Models
	5.5 Regularized Regression
		5.5.1 Tikhonov Regularization for Ridge Regression
		5.5.2 Lasso
		5.5.3 Dual Constrained Formulation
		5.5.4 Matching Pursuit
	Exercises
6 Gradient Descent
	6.1 Functions
	6.2 Gradients
	6.3 Gradient Descent
		6.3.1 Learning Rate
	6.4 Fitting a Model to Data
		6.4.1 Least Mean Squares Updates for Regression
		6.4.2 Decomposable Functions
	Exercises
7 Dimensionality Reduction
	7.1 Data Matrices
		7.1.1 Projections
		7.1.2 Sum of Squared Errors Goal
	7.2 Singular Value Decomposition
		7.2.1 Best Rank-k Approximation of a Matrix
	7.3 Eigenvalues and Eigenvectors
	7.4 The Power Method
	7.5 Principal Component Analysis
	7.6 Multidimensional Scaling
		7.6.1 Why does Classical MDS work?
	7.7 Linear Discriminant Analysis
	7.8 Distance Metric Learning
	7.9 Matrix Completion
	7.10 Random Projections
	Exercises
8 Clustering
	8.1 Voronoi Diagrams
		8.1.1 Delaunay Triangulation
		8.1.2 Connection to Assignment-Based Clustering
	8.2 Gonzalez's Algorithm for k-Center Clustering
	8.3 Lloyd's Algorithm for k-Means Clustering
		8.3.1 Lloyd's Algorithm
		8.3.2 k-Means++
		8.3.3 k-Mediod Clustering
		8.3.4 Soft Clustering
	8.4 Mixture of Gaussians
		8.4.1 Expectation-Maximization
	8.5 Hierarchical Clustering
	8.6 Density-Based Clustering and Outliers
		8.6.1 Outliers
	8.7 Mean Shift Clustering
	Exercises
9 Classification
	9.1 Linear Classifiers
		9.1.1 Loss Functions
		9.1.2 Cross-Validation and Regularization
	9.2 Perceptron Algorithm
	9.3 Support Vector Machines and Kernels
		9.3.1 The Dual: Mistake Counter
		9.3.2 Feature Expansion
		9.3.3 Support Vector Machines
	9.4 Learnability and VC dimension
	9.5 kNN Classifiers
	9.6 Decision Trees
	9.7 Neural Networks
		9.7.1 Training with Back-propagation
10 Graph Structured Data
	10.1 Markov Chains
		10.1.1 Ergodic Markov Chains
		10.1.2 Metropolis Algorithm
	10.2 PageRank
	10.3 Spectral Clustering on Graphs
		10.3.1 Laplacians and their EigenStructures
	10.4 Communities in Graphs
		10.4.1 Preferential Attachment
		10.4.2 Betweenness
		10.4.3 Modularity
	Exercises
11 Big Data and Sketching
	11.1 The Streaming Model
		11.1.1 Mean and Variance
		11.1.2 Reservoir Sampling
	11.2 Frequent Items
		11.2.1 Warm-Up: Majority
		11.2.2 Misra-Gries Algorithm
		11.2.3 Count-Min Sketch
		11.2.4 Count Sketch
	11.3 Matrix Sketching
		11.3.1 Covariance Matrix Summation
		11.3.2 Frequent Directions
		11.3.3 Row Sampling
		11.3.4 Random Projections and Count Sketch Hashing
	Exercises
Index