UNIT 016 h allocated
Recommendation data
- Explicit and implicit feedback
- exposure
- cold start
- negative sampling
← Course catalogue
MACHINE LEARNING / SPECIALIST
ML250Recommender systems & ranking
24 h productive allocation9 h 36 m learning + 14 h 24 m training4 units · 1 assessed practical brief
Detailed course plan and training brief. A complete taught course pack is in preparation. The linked foundation module, where shown, is a smaller available resource.
BEFORE YOU BEGIN
A clear starting point.
Specialist studio; environment and faculty must be confirmed before scheduling
WHAT YOU SHOULD BE ABLE TO DEMONSTRATE
- Explain the assumptions and failure modes in recommendation data.
- Complete the practical: build a recommendation baseline and measure ranking quality, coverage and cold-start behaviour.
- Defend the resulting ranking benchmark with reproducible evidence.
THE SUBJECTS / FOUR CONNECTED UNITS
What you will study.
UNIT 026 h allocated
Candidate generation
- Popularity baseline
- nearest neighbours
- collaborative filtering
- embeddings
UNIT 036 h allocated
Ranking objectives
- Pointwise and pairwise learning
- ranking losses
- diversity
- calibration
UNIT 046 h allocated
Evaluation
- Recall at K
- NDCG
- temporal splits
- offline versus online outcomes
THE TRAINING STUDIO
Make the subject
visible in your work.
Build a recommendation baseline and measure ranking quality, coverage and cold-start behaviour.
Evidence to produce
- Ranking benchmark
- coverage analysis
- model card
Training brief for supervised delivery. Code, datasets, answer keys and environment acceptance must be completed in the full course pack before delivery is confirmed.
ASSESS THE REASONING
A question you
should be able to answer.
Why can historical clicks reflect exposure bias rather than pure preference?
Review dimensions
Technical correctness · reproducibility · interpretation of results · failure analysis · individual explanation. Course-level rubric and faculty moderation form part of the final adopted teaching pack.
External reference access, hardware, cloud accounts and paid services are separate. No third-party certification is included by implication.