Interactive guides translating math into code — for ML, robotics, quantum computing, autonomous vehicles, and beyond
Philosophy: These guides assume you're a software engineer with minimal math background. Every concept is grounded in something physical, connected to real systems (GPS, autonomous vehicles, sensor fusion, ML), and presented with interactive visualizations. Math isn't abstract — it's the literal code running in every GPS receiver, robot, and ML training loop.
Approach: Think in functions, loops, and data structures. Derivatives are rates. Integrals are for-loops that sum. Differential equations are update rules. Linear algebra is array operations. Trigonometry is coordinate transforms. The notation is just syntax.
The full pipeline from algebra to the Kalman filter, Khan Academy style. Prerequisite math for ML, robotics, quantum computing, and autonomous vehicles — packed with worked examples, practice problems, and interactive visualizations.
Covers: Algebra · Functions · Limits · Exponents & Logs · Derivatives (incl. Partials & Gradients) · Integrals · Differential Equations · State Space · Numerical Methods · PID Control · Bayes' Theorem · Statistics · Kalman Filter · ML & Quantum applications throughout
Angles, triangles, circles, and waves. Essential for rotation, navigation, oscillation, and signal processing. Referenced throughout the engineering math guide.
Covers: Unit Circle · Sin/Cos/Tan · Radians · Pythagorean Identity · Angle Addition · Law of Sines/Cosines · Polar Coordinates · Waves · Fourier Basics
Vectors, matrices, transformations, and eigenvalues. The language of state space, computer graphics, neural networks, and multi-dimensional systems.
Covers: Vectors · Dot Product · Cross Product · Matrices · Matrix Multiplication · Determinants · Inverse · Eigenvalues/Eigenvectors · Transformations · Least Squares
The foundation of all math and CS. Sets, subsets, unions, intersections, complements, Venn diagrams, Cartesian products, and De Morgan's laws — with code equivalents.
Covers: Sets · Notation · Subsets · Union · Intersection · Difference · Complement · Venn Diagrams · Cartesian Product · Power Set · De Morgan's Laws · Inclusion-Exclusion
Counting without counting. Factorials, permutations, combinations, Pascal's Triangle, and the formulas that tell you "how many ways?" for any arrangement or selection problem.
Covers: Counting Principle · Factorials · Permutations · Combinations · Repetition · Pascal's Triangle · Stars & Bars · Pigeonhole Principle
Making decisions under uncertainty. From basic probability to Bayes' Theorem, distributions, expected value, variance, and the Central Limit Theorem — all with real-world software examples.
Covers: Probability · Conditional Probability · Bayes' Theorem · Independence · Distributions · Expected Value · Variance · Normal Distribution · Descriptive Stats · CLT
If you're new to math for engineering: Start with Complete Engineering Mathematics from the beginning. It builds sequentially — each chapter uses the previous ones. The trig and linear algebra guides are referenced when needed, but you can read them in parallel.
If you need a specific topic: Each guide has a table of contents. Jump directly to what you need. The worked examples and interactive visualizations let you learn by doing.
If you're debugging a system: Use the real-world examples. Every guide connects math to actual engineering problems: GPS errors, wheel encoders, RC circuits, PID tuning, sensor fusion, signal processing.
The three guides form a unified system: