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Data Science Topics

0.0.1

General

1. Missing Data
2. Principal Component Analysis
3. Principal Component Analysis - Iris
4. Dimension Reduction or Concept Learning?
5. Latent Dirichlet Allocation
6. Conditional Multivariate Normal Distribution
7. Conditional Bivariate Gaussians
8. Conditional Multivariate Gaussian, In Depth
9. Entropy and Mutual Information
10. Mutual Information for Gaussian Variables
11. Conditional Mutual Information for Gaussian Variables
12. Partial Correlation
13. Precision-Recall and Receiver Operating Characteristic Curves
14. Kalman Filter, I
15. Kalman Filter, II
16. Gaussian Hidden Markov Models
17. Iterative Proportional Fitting
18. Iterative Proportional Fitting, Higher Dimensions
19. One-Way ANOVA
20. Optimizing a Function
21. Brier Score
22. Log Loss
23. Spherical Payoff
24. Proper Scoring Rules and Model Calibration

Distributions

1. Generating Normally Distributed Values
2. Kullback-Leibler Divergence
3. Dirichlet-Multinomial Distribution
4. Schedule Risk Analysis Distributions
5. Gaussian Mixture Models
6. Dirichlet and Guassian Mixture Models
7. Data Discretization and Gaussian Mixture Models
8. K-means, BIC, AIC
9. PDFs and CDFs
10. s-curves
11. Markov Chain, Stationary Distribution

Regression

1. Correlation vs Regression Coefficient
2. Regression Errors
3. Psuedo r-squared for logistic regression
4. McFadden’s Psuedo R^2
5. Logistic Regression and Naive Bayes
6. Estimating Standard Error and Significance of Regression Coefficients
7. Logistic Regression on Probabilities
8. Safe and Strong Screening for Generalized LASSO
9. Log-linear Models for Three-way Tables
10. Log-Linear Models and Graphical Models
11. Linear Mixed Model
12. Iteratively Reweighted Least Squares Regression
13. Linear Mixed-Effect Regression

Survival Analysis

1. Survival Analysis Functions
2. Multivariate Survival Analysis

Rating and Ranking

1. Bradley-Terry-Luce Model
2. Massey’s Method
3. Massey’s Method, Offense and Defense
4. Colley’s Method
5. Keener’s Method
6. Markov Method
7. Reordering Method

Gradient Descent

1. Gradient descent
2. Regression, Gradient Descent
3. Logistic Regression, Gradient Descent
4. Poisson Regression, Gradient Descent
5. Neural Networks, Gradient Descent
6. Stochastic Gradient Descent for Online Learning
7. Differentiation and Gradient

Natural Language Processing

1. Latent Semantic Analysis
2. Topic Modeling with Gensim
3. Classification with GPT/Ollama Embeddings

Deep Learning

1. Artificial Neural Network
2. Restricted Boltzmann Machine
3. Recurrent Neural Network (RNN), Classification
4. Pose Estimation
5. Outlier Detection with Autoencoders
6. Data Imputation with Autoencoders
7. Autoencoders, Detecting Malicious URLs

COVID-19

1. COVID Hubei Data
2. Differential Diagnosis of COVID-19 with Bayesian Belief Networks

Chernoff Faces

1. Chernoff Faces
2. Chernoff Faces, Deep Learning
3. Chernoff Faces, Classification
4. Chernoff Faces, Inception v3

Causality

1. Simpson’s Paradox
2. d-separation, Active Paths
3. d-separation, Graph Transformation
4. Backdoor Criterion
5. Frontdoor/Backdoor Adjustment Formulas
6. Generating Random Bayesian Network
7. Creating a Junction Tree
8. Inference in Gaussian Networks
9. Causal Inference
10. Dynamic Bayesian Network, Markov Chain
11. Dynamic Bayesian Networks, Hidden Markov Models
12. Propensity Score Matching
13. Estimating Conditional Probabilities
14. Explanation vs Prediction
15. Explanation vs Prediction, Imbalanced Data
16. Frisch-Waugh-Lowell Theorem
17. Pearl Causal Hierarchy

Retail

1. Pricing Elasticity of Demand
2. Pricing Elasticity of Demand Modeling
3. Optimizing Marginal Revenue from the Demand Curve
4. Optimizing Marginal Revenue from the Demand Curve, Kaggle
5. Multi-Objective Optimization for Demand Curve
6. Demand Curve Fitting
7. Modeling Non-linear Pricing Elasiticity

References

Bibliography

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