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.
E35 Bayesian and derivative-free optimization
Advanced pathE: Proposed extensionsplannedabout 100 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
Used to tune models and experiments when gradients are unavailable.
Prerequisites
- Required
- A02 Convex & numerical optimization, B11 Probability I, E32 Kernel methods and Gaussian processes
- Recommended
- none
- Can be taken at the same time
- none
- Everything required, transitively (18 courses)
- A02, B01, B02, B03, B04, B05, B06, B08, B09, B11, B12, B13, B19, B20, E32, F01, F02, F03
- Courses that require this one
- none
Concept-level units
Units follow the syllabus order, as a recommended sequence rather than a gate.
- Black-box functions and sample complexity
- Gaussian-process surrogates
- Acquisition functions: expected improvement, UCB, Thompson sampling
- Regret bounds
- Trust-region and direct-search methods
- Bandits and hyperparameter search
- Constraints and noise
- Batch and high-dimensional methods
Assessable outcome
Implement and analyse a Bayesian optimizer and state its regret guarantee and assumptions.
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.
- Frazier · A Tutorial on Bayesian Optimization (free)
- Conn, Scheinberg, Vicente · Introduction to Derivative-Free Optimization
- Garnett · Bayesian Optimization (free)
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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