AI-fundamenten

• Explain training and inference in plain language.
• Separate demonstrated capability from marketing claims.
• Evaluate AI outputs using evidence, uncertainty, and fit-for-purpose tests.

Prerequisites:None.

1. 1

   What AI is

   Outcome: Distinguish AI, machine learning, and ordinary software.

   Practice: Classify five everyday systems and explain which definition of AI you used.

   AI concepts and boundaries
   Wat is AI? →Basisprincipes van machinaal leren →
2. 2

   How systems learn

   Outcome: Describe the role of examples, objectives, and generalization.

   Practice: Diagnose why a model can perform well in testing but fail for a new population.

   Data, training, and generalization
   Hoe AI leert →Basisprincipes van machinaal leren →AI-training →
3. 3

   Models and inference

   Outcome: Explain what happens when a trained model produces an output.

   Practice: Trace one user request from input through model output and human review.

   Models, inference, and limitations
   AI-modellen uitgelegd →AI-inferentie →Neurale netwerken →
4. 4

   Evidence and uncertainty

   Outcome: Check claims, benchmarks, and confident-sounding outputs.

   Practice: Create a claim ledger for one AI product announcement.

   Evaluation and benchmark literacyUncertainty and confidence calibrationSource and claim verification
   AI-benchmarks →AI-hallucinaties →AI-mythen →