← All categoriesMath & Statistics
The probability, linear algebra and calculus that every ML interview quietly assumes you know.
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40🟢Bayes’ Theorem4 min🟢Central Limit Theorem4 min🟢Law of Large Numbers4 min🟢Expectation & Variance4 min🟢Covariance vs Correlation4 min🟢Distributions You Must Know5 min🟢Type I vs Type II Errors4 min🟢Vector Norms (L1, L2, L∞)4 min🟢Dot Product & Cosine Similarity4 min🟢Correlation vs Causation4 min🟡Conditional Independence4 min🟡Maximum Likelihood Estimation4 min🟡MAP vs MLE4 min🟡Hypothesis Testing & p-values5 min🟡Statistical Power & Sample Size4 min🟡Confidence Intervals4 min🟡A/B Testing End to End5 min🟡Bayesian vs Frequentist5 min🟡Eigenvectors & Eigenvalues4 min🟡Rank, Invertibility & Null Space5 min🟡Gradients, Jacobians & Hessians5 min🟡Chain Rule Behind Backprop5 min🟡Convexity & Why It Matters5 min🟡Entropy, Cross-Entropy & KL Divergence5 min🟡The Curse of Dimensionality5 min🟡Simpson’s Paradox4 min🟡Markov Chains5 min🔴Conjugate Priors4 min🔴A/B Test Pitfalls: Peeking & Novelty5 min🔴Multiple Testing & Bonferroni5 min🔴Singular Value Decomposition5 min🔴LU, QR & Cholesky5 min🔴Positive Definite Matrices4 min🔴Taylor Expansions in Optimization5 min🔴Lagrange Multipliers5 min🔴JS Divergence & Wasserstein Distance5 min🔴Mutual Information5 min🔴Sampling: Bootstrap, MCMC, Importance5 min🔴Causal Inference: DAGs & Confounders5 min🔴Propensity Score Matching5 min