Data Science Handbook
Data Science Handbook
Foundations
Math
Combinatorics
Vector Spaces
Geometry
Linear Algebra
Linear Programming
Non-linear Programming
Puzzles
Probabilities
Expectation and Variance
Correlation and Dependence
Bayesian’s Theorem
Markov Chain
Sampling
Multivariate Notations
Exponential Families
Large Sample Theory
Statistics
Sample Survey
Randomized Controlled Trials
Hypothesis Testing
Common Tests
Confusion Matrix
Maximum Likelihood Estimator
Estimators Evaluation
Tools
Python
R
SQL
LaTeX
MyST Markdown
Algorithms
Algorithms Concepts
Bisection Search
Polynomial Reduction
\(P\)
and
\(NP\)
Randomized Algorithms
Streaming Algorithms
Greedy Algorithms
Interval Scheduling
Huffman Coding
Dynamic Programming
Weighted Interval Scheduling
Longest Common Subsequence
Longest Increasing Subsequence
Largest Sum Subsequence
Minimum Knapsack
Chain Matrix Multiplication
Graph Related
Shortest Path
Minimum Spanning Tree
Maximum Flow
Matching
Maximum Independent Set in Trees
LP on Max-flow and Min-cut
Machine Learning
Machine Learning Basics
Taxonomy
Information Theory
Kernels
Data Issues
Model Selection
Semi-supervised Learning
Self-supervised Learning
Fourier Transform-based Representations
Regression
Linear Models - Estimation
Linear Models - Inference
Linear Models - Diagnosis
Linear Models - Advanced Topics
Generalized Linear Models
Logistic Regression
Multinomial Logistic Regression
Ordinal Logistic Regression
Poisson Regression
Multivariate Regression
Penalized Regression
Classification
K-nearest neighbors
For Gaussian Data
Linear Discriminant Analysis
Support Vector Machine
Decision Tree
Dimensionality Reduction
Principal Component Analysis
PCA Variants
Canonical Correlation Analysis
Multidimensional Scaling
Graph-based Spectral Methods
SNE and
\(t\)
-SNE
Factor Analysis
Correspondence Analysis
Independent Component Analysis
Clustering
\(k\)
-means clustering
Agglomerative Methods
Spectral Clustering
Gaussian Mixtures
Graphical Models
Random Walks in Graphs
Hidden Markov Models
Topic Models
Language Models
Computation Issues
Neural Networks
Stochastic Gradient Descent
Trainability
Regularization
Autoencoders
Variational Autoencoders
Sequential Models
Generative Adversarial Networks
Application to Density Fitting
For Graph-structured Data
Graph Basics
Descriptive Analysis
Sampling and Estimation
Modeling
Topology Inference
Processes on Graphs
Embeddings
Graphical Neural Networks
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Index