Mathematics & computational learning
Lessons that explain the mathematics used in real projects, from concept and equations to worked examples and implementation.
Linear algebra, calculus, probability, statistics, and optimization with a focus on where the mathematics appears in AI.
5 lessons
Problem modeling, objectives, constraints, evolutionary methods, and metaheuristics for decision and search problems.
3 lessons
Connecting code, mathematical models, numerical experiments, data, and reproducible implementation.
3 lessons
Applied mathematics behind system behavior, rates, scoring, and engineering algorithms.
1 lessons
Search by concept or title, then narrow the results by category and level.
3 lessons
Clear filtersConvexity, positive semidefiniteness, and the role of the Hessian in convex QP.
A mathematics lesson connecting convex structure to the Convex QP Solver.
How a decision problem becomes variables, an objective, and constraints.
A fixture for teaching the mathematical language of decision and optimization problems.
The core ideas and mathematics behind approximate search with GA and DE.
A fixture for introducing evolutionary and population-based optimization ideas.
This area is for conceptual, mathematical, and technical lessons, with real connections to projects, research, and solutions.
