Queen Mary University of London

Named groups are the most reliable public signal of where a department's attention actually sits, because they carry funding and faculty rather than only marketing copy. Whether any of them will take you is a separate question, and one worth asking directly before applying.

Recognition

The British system

A UK taught masters runs twelve months: two semesters of taught modules followed by a summer dissertation project, which is usually a third of the credit. It is a designed sequence rather than a set of electives, which makes it more predictable and less flexible than the American equivalent. The compression is real: there is little slack, and the project begins before the taught material has fully settled. Applications typically open in October for the following September and are assessed on a rolling basis, so early application matters more than it does in the US.

The programme

MSc Artificial Intelligence, at Queen Mary University of London.

Entry requirements, fees and deadlines are on that page and deliberately not reproduced here. They change every year, cannot be verified at scale, and a figure that is twelve months old is worse than no figure at all.

What to check on the syllabus

No ranking measures teaching quality, so the only way to assess a taught programme is to read what it actually contains. Six things worth looking for, in the published module list rather than the prospectus.

• Statistics and probability as prerequisites, not options. A programme that admits you without them will also teach around them.
• Evaluation as its own subject. Measuring whether a system works is the hardest part of the job and the part most often assumed rather than taught.
• Failure modes and monitoring after deployment. Models degrade and distributions shift, and almost no curriculum covers what happens after a system ships.
• A capstone with real stakeholders, not a benchmark competition. Optimising a leaderboard teaches a skill that does not transfer.
• Some governance, auditing or measurement validity. The regulatory surface around this work is growing faster than the technical one.
• Enough mathematics to read a paper. A programme that teaches frameworks produces someone who can use this year's tools.

Subfields it publishes in

General AI, Machine learning, Natural language processing. If you already know which area you want, subfield strength is a better guide than an overall position, since a department can be outstanding in one and ordinary in another. Publication-based rankings can be filtered by subfield; reputation-based ones generally cannot.

Stating the gap is part of the entry. A page that answers every question about an institution is either drawing on sources it has not named or filling in what it does not know.

Before you use any of this

The two serious rankings disagree completely about who leads AI. QS puts MIT first; CSRankings puts Peking University first and finds eighteen of the top twenty in Asia. That is a methodology difference rather than a contradiction, and the explanation, along with the rubric above in full context, is worth reading before choosing on a number.

Sources for this page

Ranking positions from the QS Data Science and Artificial Intelligence table for 2026 and the CSRankings 2025 and 2026 editions. Founding year and institutional history from the reference page below. Programme details from the institution's own admissions pages. Deliberately absent: fees, acceptance rates and entry requirements, which change annually and cannot be verified at scale.

Department and admissionsReference and imagesOnline courses instead

Common questions

Is Queen Mary University of London good for AI?

Queen Mary University of London appears in this list because of its position in at least one of the two serious rankings, or because of a documented departmental strength. It does not hold a verified top-twenty position in either table, and is included for the departmental strengths described above. Neither ranking measures teaching quality, which is what the curriculum rubric is for.

What AI programme does Queen Mary University of London offer?

MSc Artificial Intelligence. The link on this page goes to the institution's own page, which is where entry requirements, fees and application deadlines are current. Those are deliberately not reproduced here, because they change annually and a stale figure is worse than none.

Which areas of AI is Queen Mary University of London strongest in?

Based on the subfields it publishes in: general ai, machine learning, natural language processing. Subfield strength matters more than an overall ranking position if you already know what you want to work on, since a department can be outstanding in one area and ordinary in another.

How should I use university rankings when choosing?

Decide which question the ranking answers before you read it. For a PhD, publication output at top venues is the relevant signal and CSRankings measures it directly. For immediate industry hiring, employer reputation matters and QS weights it at 15%. For a taught masters, neither measures curriculum quality, so read the syllabus against the six-point rubric instead.