AI-05 · Undergraduate · ENGLISH · PLANNED
Begin with a baseline, trace a model, measure errors and improve it.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Artificial Intelligence | Machine Learning II: Unsupervised and Probabilistic Models
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Sign in to save this learning pathPractical outcome: Find and critique groupings in a synthetic customer-free dataset.
Explain distance metrics using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Explain k-means using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Explain choosing cluster count using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Explain hierarchical clustering using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Use the first concepts from this section. Find and critique groupings in a synthetic customer-free dataset. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain density-based clustering using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Explain principal component analysis using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Explain manifold embeddings using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Explain clustering validity using a visual and one worked real-world example. Context: Find and critique groupings in a synthetic customer-free dataset. End with a prediction or check-for-understanding.
Find and critique groupings in a synthetic customer-free dataset. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Distance metrics, k-means, Choosing cluster count and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Compare competing explanations for an uncertain time series.
Explain bayesian networks using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Explain conditional independence using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Explain maximum a posteriori estimation using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Explain gaussian mixtures using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Use the first concepts from this section. Compare competing explanations for an uncertain time series. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain expectation maximisation using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Explain hidden markov models using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Explain gaussian processes using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Explain posterior predictive checks using a visual and one worked real-world example. Context: Compare competing explanations for an uncertain time series. End with a prediction or check-for-understanding.
Compare competing explanations for an uncertain time series. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Bayesian networks, Conditional independence, Maximum a posteriori estimation and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Design a baseline for rare or changing patterns.
Explain anomaly detection using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Explain semi-supervised learning using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Explain active learning using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Explain online learning using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Use the first concepts from this section. Design a baseline for rare or changing patterns. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain recommendation basics using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Explain matrix factorisation using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Explain ranking objectives using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Explain concept drift using a visual and one worked real-world example. Context: Design a baseline for rare or changing patterns. End with a prediction or check-for-understanding.
Design a baseline for rare or changing patterns. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Anomaly detection, Semi-supervised learning, Active learning and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Design a baseline for rare or changing patterns. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Machine Learning II: Unsupervised and Probabilistic Models, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Data experiments, evaluations, model tracing and reproducible projects. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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