Under construction. This area is a framework being built: most courses have no teaching material yet, and things may change or break. Private self-study. Not an official programme and not recognised by any ministry or school. No qualification, mark or document is issued, and “years 1 to 3” and “advanced” are names of study paths only.
E42 Reading and evaluating AI research
Advanced pathE: Proposed extensionsplannedabout 80 h (planning estimate)
Planned: syllabus only, no teaching material yet. Nothing on this page can be studied; it describes what the course would cover.
Named in: Advanced applied mathematics pathway
Why this course
A capstone for the AI strand: read a paper the way a mathematician reads a theorem.
Prerequisites
- Required
- B17 Mathematical statistics I, E37 Modern deep-learning architectures, A09 Mathematics of statistical learning
- Recommended
- none
- Can be taken at the same time
- none
- Everything required, transitively (24 courses)
- A02, A09, B01, B02, B03, B04, B05, B06, B08, B09, B11, B12, B13, B15, B17, B19, B20, E37, F01, F02, F03, G01, G03, U09
- Courses that require this one
- none
Concept-level units
Units follow the syllabus order, as a recommended sequence rather than a gate.
- The anatomy of a machine-learning paper
- Claims, baselines and ablations
- Statistics of experiments: seeds, variance, significance
- Benchmarks, leakage and contamination
- Theory papers: assumptions versus practice
- Reproducibility
- Reading press coverage against the paper
- Writing a one-page critique
Assessable outcome
Write a critique of a published paper and of a newspaper article about it, naming what is shown and what is not.
Assessment design
- topic exercises
- mixed problem sets
- unseen proof or derivation problems
- cumulative examination
cumulative; uses spaced retrieval
Resources
Every title here is candidate, unverified unless marked verified. Titles were suggested while planning. Nobody has checked that they exist, what they cost or how they are licensed.
- Papers chosen each year from NeurIPS, ICML and ICLR proceedings
- Pineau et al · Improving reproducibility in machine learning research (paper)
- Henderson et al · Deep reinforcement learning that matters (paper)
Content record
- Version
- 0.0.0
- Review
- no review of any kind
- Provenance
- PROPOSED by the assistant on 4 Nov 2026 so that a person with a mathematics degree can read AI articles fluently; not in the owner's brief; APPROVED by the owner on 4 Nov 2026 as an elective; still needs a curriculum reviewer
- Known issues
- Approved as an elective on 4 Nov 2026; syllabus only, no lessons, candidate resources unverified, needs a curriculum reviewer.
- Type
- course
Bridges and laboratories
No bridge or laboratory is attached to this course yet.
You and this course
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