Statistics / Machine Learning Quick Notes

To be added

Fragments of things I learned.

They may be moved to a separate category later.

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Table of contents

• Stats Vocabulary / Short Notes
• Properties of Expectation, Variance, and Covariance
• Asymptotically Optimal (Efficient)
• Basic Combinatorics
• Bayes' Theorem
• Bivariate Distribution (Joint Distribution)
• Bootstrap
• Central Limit Theorem / Law of Large Numbers
• Commonly Used Distributions
• Conditional Expectation / Variance
• Confidence
• Confusion Matrix
• Convergence of Random Variables
• Correlation
• Covariance
• Delta Method
• Entropy / KL Divergence / Cross Entropy
• Estimators / Bias / Consistency
• F-Score
• Independent and Identically Distributed (IID)
• Lagrange Multipliers
• Maximum Likelihood
• Moment / Expectation / Variance
• Mutivariate Distributions
• Non-Parametric Inference
• Permutation Test
• Pooled Variance
• Random Variable Transformation
• Regression
• Sample Mean and Variance
• Score Function and Fisher Information
• Spurious Correlation
• Wald Test