Numerical Methods and Scientific Computing

Controlled numerical approximation: floating point, conditioning, roots, interpolation, quadrature, direct and iterative linear algebra, ODE integration, optimization, convergence, and reproducible verification.

By @apv

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Course details

About this course

Level: intermediate
Language: en-CA
License: CC-BY-4.0
Duration: Not declared
Curriculum: Not declared
Prerequisites: Not declared
Attribution: APV, Theoria

Technical metadata

Package format and contents

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Format version: MCF 1.1
Lessons: 37
Activities: 64
Questions: 189
Files / assets: Not reported / Not reported
Validation: valid

Learning outcomes

What learners can expect

Reason about floating-point representation, cancellation, forward/backward error, conditioning, and algorithmic stability.
Implement and verify root finding, interpolation, finite differences, quadrature, and scalar optimization methods.
Solve dense and sparse linear systems with pivoting, factorization, conditioning, refinement, and iterative methods.
Implement explicit ODE methods, Runge-Kutta schemes, convergence studies, and stability/stiffness diagnostics.
Build reproducible scientific-computing workflows with benchmarks, tests, error budgets, and numerical/model-error separation.

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Version history

0.2.0course · MCF 1.1 · 518.7 KiBAug 12, 2026

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