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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="description" content="A focused mathematics pathway for AI and machine learning students." />
<title>Math For AI</title>
<link rel="icon" type="image/svg+xml" href="favicon.svg" />
<link rel="stylesheet" href="site.css" />
</head>
<body class="home-page">
<div class="sitebar">
<div class="sitebar__inner">
<a class="sitebar__brand" href="index.html" aria-label="Math For AI home">
<span class="sitebar__mark" aria-hidden="true">M</span>
<span>Math For AI</span>
</a>
<nav class="sitebar__links" aria-label="Site">
<a class="sitebar__link" href="#modules">Modules</a>
<a class="sitebar__link" href="#foundation">Foundation</a>
<button class="theme-toggle" type="button" data-theme-toggle aria-label="Switch theme"></button>
</nav>
</div>
</div>
<main>
<header class="home-hero">
<div class="hero-layout">
<div>
<div class="eyebrow">Expert path for machine learning</div>
<h1 class="hero-title">Math For AI</h1>
<p class="hero-copy">
A clean, ordered curriculum for the mathematics behind modern AI, built for students who want strong intuition and practical ML context.
</p>
<div class="hero-actions">
<a class="btn btn-primary" href="linear_algebra_ml.html">Start learning</a>
<a class="btn btn-ghost" href="#modules">View modules</a>
</div>
</div>
<div class="hero-visual" aria-label="Math For AI curriculum map">
<div class="path-map">
<div class="path-node is-active"><span>Step 01</span><strong>Linear Algebra</strong></div>
<div class="path-node"><span>Step 02</span><strong>Calculus</strong></div>
<div class="path-node"><span>Step 03</span><strong>Optimization</strong></div>
<div class="path-node"><span>Step 04</span><strong>Probability</strong></div>
<div class="path-node"><span>Step 05</span><strong>Statistics</strong></div>
<div class="path-node"><span>Step 06</span><strong>Information Theory</strong></div>
<div class="path-node"><span>Step 07</span><strong>Geometry</strong></div>
<div class="path-node"><span>Step 08</span><strong>Probabilistic Linear Algebra</strong></div>
<div class="path-node"><span>Step 09</span><strong>Factorization</strong></div>
</div>
</div>
</div>
</header>
<section class="home-section" id="foundation">
<div class="section-head">
<div>
<div class="eyebrow">Foundation</div>
<h2>One consistent path from math to ML intuition.</h2>
</div>
<p>
Each module is structured around core concepts, formulas, applied ML context, interview-style questions, exercises, and resources.
</p>
</div>
<div class="metric-grid">
<div class="metric">
<strong>9</strong>
<span>focused modules</span>
</div>
<div class="metric">
<strong>AI</strong>
<span>first-principles explanations</span>
</div>
<div class="metric">
<strong>ML</strong>
<span>practical application context</span>
</div>
</div>
</section>
<section class="home-section" id="modules">
<div class="section-head">
<div>
<div class="eyebrow">Modules</div>
<h2>Study in sequence.</h2>
</div>
<p>
The order moves from representation and change to uncertainty, information, geometry, and dimensionality reduction.
</p>
</div>
<div class="module-grid">
<a class="module-card" href="linear_algebra_ml.html">
<span class="module-card__step">Step 01</span>
<h3>Linear Algebra</h3>
<p>The backbone of machine learning representations.</p>
<ul>
<li>Scalars, vectors, matrices, tensors</li>
<li>Vector and matrix operations</li>
<li>Dot product and geometric interpretation</li>
<li>Matrix multiplication</li>
<li>Identity, transpose, inverse</li>
<li>Rank of a matrix</li>
<li>Systems of linear equations</li>
<li>Linear transformations</li>
<li>Column space and null space (intuition)</li>
<li>Eigenvalues and eigenvectors (intuition + usage)</li>
<li>Orthogonality</li>
<li>Norms and distances (L1, L2)</li>
</ul>
</a>
<a class="module-card" href="calculus_ml.html">
<span class="module-card__step">Step 02</span>
<h3>Calculus</h3>
<p>The language of change, gradients, and learning.</p>
<ul>
<li>Functions and graphs</li>
<li>Limits (intuition only)</li>
<li>Derivatives and gradients</li>
<li>Partial derivatives</li>
<li>Chain rule (very important)</li>
<li>Gradient as direction of steepest descent</li>
<li>Local vs global minima</li>
<li>Convex vs non-convex functions</li>
</ul>
</a>
<a class="module-card" href="OptimizationConcepts.html">
<span class="module-card__step">Step 03</span>
<h3>Optimization</h3>
<p>How models improve through loss and updates.</p>
<ul>
<li>Gradient descent</li>
<li>Learning rate intuition</li>
<li>Cost and loss functions</li>
<li>Saddle points</li>
<li>Vanishing and exploding gradients (intuition)</li>
</ul>
</a>
<a class="module-card" href="ProbabilityTheory.html">
<span class="module-card__step">Step 04</span>
<h3>Probability Theory</h3>
<p>Reasoning clearly under uncertainty.</p>
<ul>
<li>Random variables</li>
<li>Discrete vs continuous variables</li>
<li>Probability distributions</li>
<li>PMF, PDF, CDF</li>
<li>Expectation and variance</li>
<li>Common distributions: Bernoulli, Binomial, Normal, Uniform, Poisson</li>
<li>Independence and conditional probability</li>
<li>Bayes' theorem</li>
<li>Likelihood vs probability</li>
</ul>
</a>
<a class="module-card" href="statistics_ml.html">
<span class="module-card__step">Step 05</span>
<h3>Statistics</h3>
<p>From samples and variation to decisions.</p>
<ul>
<li>Population vs sample</li>
<li>Measures of central tendency</li>
<li>Measures of dispersion</li>
<li>Covariance</li>
<li>Correlation</li>
<li>Bias and variance</li>
<li>Sampling techniques</li>
<li>Central Limit Theorem (intuition)</li>
<li>Law of Large Numbers</li>
<li>Outliers and robustness</li>
</ul>
</a>
<a class="module-card" href="information_theory_ml.html">
<span class="module-card__step">Step 06</span>
<h3>Information Theory</h3>
<p>Entropy, surprise, and classification loss.</p>
<ul>
<li>Entropy</li>
<li>Cross-entropy</li>
<li>KL divergence</li>
<li>Information gain</li>
<li>Why cross-entropy is used in classification</li>
</ul>
</a>
<a class="module-card" href="geometry_distances_ml.html">
<span class="module-card__step">Step 07</span>
<h3>Geometry and Distances</h3>
<p>Similarity, space, and high-dimensional intuition.</p>
<ul>
<li>Euclidean distance</li>
<li>Manhattan distance</li>
<li>Cosine similarity</li>
<li>Angle between vectors</li>
<li>High-dimensional intuition (curse of dimensionality)</li>
</ul>
</a>
<a class="module-card" href="prob_linalg_ml.html">
<span class="module-card__step">Step 08</span>
<h3>Probability + Linear Algebra</h3>
<p>The bridge to multivariate modeling.</p>
<ul>
<li>Multivariate distributions</li>
<li>Gaussian distribution in higher dimensions</li>
<li>Covariance matrix interpretation</li>
<li>Mahalanobis distance</li>
</ul>
</a>
<a class="module-card" href="matrix_factorization_ml.html">
<span class="module-card__step">Step 09</span>
<h3>Matrix Factorization</h3>
<p>High-value decompositions for ML systems.</p>
<ul>
<li>Eigen decomposition</li>
<li>Singular Value Decomposition (SVD)</li>
<li>PCA math intuition</li>
<li>Dimensionality reduction rationale</li>
</ul>
</a>
</div>
</section>
</main>
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