intuitiveai

Mathematics for AI and Machine Learning

Connect vectors, derivatives, probability, statistical inference, optimization, and learning theory to the models you train and evaluate.

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Prerequisites

  1. 01
    Linear Algebra

    Vectors, Matrices, Eigenvalues, and SVD.

  2. 02
    Calculus & Gradients

    Understand slopes, partial derivatives, the chain rule, and gradient descent through visual and coded experiments.

  3. 03
    Probability

    Understanding randomness and Bayesian vs Frequentist views.

  4. 04
    Distributions

    Normal, Binomial, and Poisson distributions.

  5. 05
    Hypothesis Testing

    p-values, Z-tests, and T-tests.

  6. 06
    Convex Optimization & KKT

    Lagrange multipliers, KKT optimality conditions, Slater condition, and duality.

  7. 07
    Statistical Learning Theory

    VC Dimension, Rademacher complexity, generalization error bounds, and PAC learning.