One course · Two parts
AI, Finance and Central Banking
This is one course in two parts, formed in August 2026 by combining the Finance and AI course with the Central Banking course. Part I asks when an asset loss becomes a credit contraction, how a bank whose assets still have value can fail in a day, and what prediction and language models may and may not add to financial judgement. Part II follows the other side of the system: how a central bank's words move expectations, and how that influence is measured as evidence. Everything from both courses is on this site, each part in its original order.
Part I · Four teaching days
Finance and AI
A financial mechanism is established, then measured, then used to predict, then required to produce evidence a responsible person can check. Days 1 and 2 are finance: capital, lending capacity and runs. Days 3 and 4 open the machines and put them to work on twenty real deployments.
Part II · Four lectures
Central Banking
How words become policy: from communication and credibility to measured expectations, policy surprises, and public interpretation. Four interactive lectures trace how a central-bank message becomes a measurable, defensible claim, with twenty-five illustrated mechanisms along the way.
The course at a glance
| Part | Session | Topic |
|---|---|---|
| Part I | Day 1 | When does an asset loss become a credit contraction? |
| Part I | Day 2 | Banking: lending capacity and runs |
| Part I | Day 3 | How modern models learn, represent, predict, and generate |
| Part I | Day 4 | Twenty cases, the models behind them, and what to do next |
| Part II | Lecture 1 | Words, credibility, institutions |
| Part II | Lecture 2 | Measuring hidden expectations |
| Part II | Lecture 3 | From decisions to policy news |
| Part II | Lecture 4 | From publication to reception |
