Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output

Risk Academy, led by the Nexus Consortium · Course catalogue · Risk Science and Methods

Quantitative Risk Analysis

AI and machine learning for risk analysis

AI and machine learning for risk analysis, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

AI and machine learning for risk analysis: foundations and practice

This area covers the machine-learning methods risk analysts now use: classification and regression of risk outcomes, emulators of slow physical models, anomaly detection and the quantification of uncertainty in model outputs.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
Planned
Prerequisites
An L1 course in the area, or an entry review

Why now

Machine learning weather models (GraphCast, 2023; GenCast, 2024) now match or beat physics ensembles on many scores, ECMWF runs its AIFS operationally since 2025, and AI flood forecasting reaches ungauged basins, so risk analysts must know when to trust these models. Science, Learning skillful medium-range global weather forecasting (Lam et al.), 2023

Learning outcomes

  1. Define emulator and the other key terms of the discipline and use them correctly in a written case
  2. Explain supervised learning for risk outcomes and emulators and surrogates of physical and catastrophe models with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against uncertainty quantification in machine learning, stating evidence, date and limits

Modules

  1. Supervised learning for risk outcomes
    • Supervised learning for risk outcomes
    • Emulators and surrogates of physical and catastrophe models
    • Uncertainty quantification in machine learning
  2. Conformal prediction and distribution-free coverage
    • Conformal prediction and distribution-free coverage
    • Anomaly detection and change detection
    • Geospatial foundation models and satellite embeddings
  3. Causal inference and machine learning
    • Causal inference and machine learning
    • Validation, drift monitoring and model risk for machine learning
    • Fairness and bias in risk models
  4. Lab: the discipline's method
    • A learned model gives a point prediction with no promise attached; holding out a calibration set, ranking its residuals and taking a quantile wraps the prediction in an interval whose coverage is assured without any assumption about the model, and the size of the calibration set sets how tight that guarantee is.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. Vovk, Gammerman and Shafer, Algorithmic Learning in a Random World, 2005
  2. Angelopoulos and Bates, Conformal prediction: a gentle introduction, Foundations and Trends in Machine Learning, 2023
  3. Guo, Pleiss, Sun and Weinberger, On calibration of modern neural networks, ICML, 2017
02Live map

Connected activity in South Africa

Night-time lights and population density, the footprint of electrified, connected activity, over South Africa.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Connected activity

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Connected activity, framed on South Africa.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Optional layers (3 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

What is changing, with standards and evidence

AI and machine learning for risk analysis: frontier methods

The methods, data, tools and studies that changed AI and machine learning for risk analysis between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
AI and machine learning for risk analysis: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Machine learning weather models (GraphCast, 2023; GenCast, 2024) now match or beat physics ensembles on many scores, ECMWF runs its AIFS operationally since 2025, and AI flood forecasting reaches ungauged basins, so risk analysts must know when to trust these models. Science, Learning skillful medium-range global weather forecasting (Lam et al.), 2023

Learning outcomes

  1. Explain what changed in AI and machine learning for risk analysis since 2024 and cite the studies, data or rules behind each change
  2. Reproduce ML for hazards on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. ML for hazards
    • Graph and transformer weather models
    • Probabilistic ML ensembles
    • Flood forecasting in ungauged basins
  2. Uncertainty
    • Conformal prediction
    • Calibration and proper scoring rules
    • Distribution shift and extremes
  3. Governance of ML in risk
    • NIST AI RMF for risk models
    • Documentation: model cards and data sheets
    • Validation against physics baselines
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist

Labs with Academy tools

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. Science, Learning skillful medium-range global weather forecasting (Lam et al.), 2023
  2. Nature, Probabilistic weather forecasting with machine learning (Price et al.), 2024
  3. Nature, Global prediction of extreme floods in ungauged watersheds (Nearing et al.), 2024
  4. arXiv, A gentle introduction to conformal prediction (Angelopoulos and Bates), 2021
  5. NIST, AI Risk Management Framework, 2023
  6. Vovk, Gammerman and Shafer, Algorithmic Learning in a Random World, 2005
  7. Angelopoulos and Bates, Conformal prediction: a gentle introduction, Foundations and Trends in Machine Learning, 2023
  8. Guo, Pleiss, Sun and Weinberger, On calibration of modern neural networks, ICML, 2017
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Emulator

A fast statistical or machine-learning model trained to reproduce the outputs of a slower physical or catastrophe model.

Term

Conformal prediction

A method that wraps any model to give prediction sets with a stated coverage under exchangeability.

Term

Distribution shift

A change between the data a model was trained on and the data it is used on, which can invalidate its accuracy.

Term

Calibrated classifier

A classifier whose predicted probabilities match observed frequencies.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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