HumenhukAIGP Study Guide
Module 8: AI Governance Vocabulary

Learning techniques and methods

Reviewed by Victor Humenhuk. Published . Reviewed .

The named techniques and model types that show up as one-line scenario answers - including federated learning (data never leaves the site), transfer learning, fine-tuning, and the discriminative-vs-generative split.

These are the named techniques and model types the exam drops into one-line scenarios. Several are easy to confuse, so anchor each to its distinguishing move.

  • Active learning → the model flags the most informative unlabelled examples and asks a human to label them, cutting labelling cost.
  • Adaptive learning → systems that adjust behaviour or content in response to new data and user interactions over time (personalised tutoring).
  • Transfer learning model → reuses knowledge from one task as the starting point for a related task, saving data and compute.
  • Federated learning → local sites train a shared model on their own data and only the results aggregate centrally; the data never leaves the site.
  • Fine-tuning → further training a pre-trained model on a smaller, task- or domain-specific dataset to specialise it.
  • Classification model vs Clustering → classification predicts a discrete category (supervised); clustering groups by similarity with no labels (unsupervised).
  • Decision tree / Random forest / Bootstrap aggregating (bagging) → a single interpretable tree; an ensemble of trees on random subsets; and the resampling-with-replacement method that reduces variance.
  • Greedy algorithms → locally optimal choice at each step, fast but not guaranteed globally optimal.
Discriminative vs generative

A Discriminative model learns the boundary between classes to classify - it distinguishes rather than creates. Generative AI (next cluster) creates new content. This is one of the eight planted confusion pairs.

Don't confuse the look-alikes

Federated learning (privacy: data stays put) vs transfer learning (efficiency: reuse a prior model) vs active learning (labelling: ask a human about the hard cases) vs fine-tuning (specialise a pre-trained model). Each answers a different problem.

Key terms - quick answers

What is “Active learning”?
Model flags the most informative unlabelled examples for a human to label.
What is “Adaptive learning”?
Systems adjusting behaviour/content in response to new data and user interactions over time.
What is “Transfer learning model”?
Reuses knowledge from one task as a starting point for a related task.
What is “Federated learning”?
Local systems train a shared model on their own data; only results aggregate centrally.

Sources and study method

This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.