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 filtersFrom Lagrangians and KKT conditions to barrier methods and Convex QP.
A fixture for numerical error, step size, solution procedure, and interpretation.
From equation residuals to independent checks of numerical solutions.
A fixture for combining Python programming with numerical computation and analysis.
From residuals and norms to condition numbers and numerical sensitivity.
A scientific-computing lesson about error, sensitivity, and validation.
This area is for conceptual, mathematical, and technical lessons, with real connections to projects, research, and solutions.
