Learn Computing Mathematics with AI

AT A GLANCE

Is this programme for you?

Built for developers, data, ml engineers. AI helps you move faster; every output is checked against references, runnable examples or a human review before you rely on it.

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Who it's for: Developers, data, ML engineers
IT domain: Mathematics Statistics and Computing (Foundation to intermediate)
Prerequisites: Basic school mathematics
How AI is used: AI-assisted learning
Reviewed output: Worked computing-math and probability notebook
How work is checked: Check against references, runnable examples or domain review; record uncertain outputs
Trainer: Trainer to assign
Status: Planned · dates and fees not yet set

CORE CONCEPTS

10 concepts, each with a practice brief.

Each concept ends in evidence you can show. Briefs are starting points and are reviewed by the trainer before publication.

01
Logic and Boolean algebra
Create a worked example for Logic and Boolean algebra; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-01
02
Sets relations and functions
Create a worked example for Sets relations and functions; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-02
03
Combinatorics
Create a worked example for Combinatorics; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-03
04
Graph theory foundations
Create a worked example for Graph theory foundations; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-04
05
Vectors and matrices
Create a worked example for Vectors and matrices; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-05
06
Probability and conditional probability
Create a worked example for Probability and conditional probability; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-06
07
Descriptive statistics
Create a worked example for Descriptive statistics; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-07
08
Sampling and uncertainty
Create a worked example for Sampling and uncertainty; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-08
09
Optimization and gradients
Create a worked example for Optimization and gradients; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-09
10
Numerical precision and stability
Create a worked example for Numerical precision and stability; show inputs, outcomes and one counterexample or edge case.
ExplainFoundation to intermediateEvidence: Worked example and reasoningIWA-C45-10