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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Shortest Path

By Dennis Zheng
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