Monday, 22 June 2026
| 12:45 | Join in |
| 13:00 | Welcome, Motivation & Introduction |
| 13:15 | Recap: Explainable AI (xAI) for anomaly detection and time-dependent data |
| – global vs. local explanations | |
| – model-agnostic vs. model-specific methods | |
| – model justification | |
– specifics for time series (temporal dependence, feature engineering) | |
| 13:30 | Guided end-to-end xAI exercise on transaction data (Credit Card Fraud) |
| – exploratory data analysis | |
| – understanding data characteristics (imbalance, anonymized features) | |
| – anomaly detection / classification modelling | |
| – explanation methods (feature importance, SHAP) | |
Hands-on lab: training a lightweight model and interpreting fraud predictions | |
| 14:15 | Break |
| 14:30 | Guided end-to-end xAI exercise on time series data (NYC Taxi) |
| – time series exploration (trend, seasonality, anomalies) | |
| – modelling (baseline + anomaly detection via residuals / Orion reference) | |
| – explanation methods for temporal data (lag features, residual analysis, SHAP) | |
Hands-on lab: detecting and explaining anomalies in time series | |
| 15:15 | Break |
| 15:45 | Comparison, limitations & discussion |
| – transferability of xAI methods across data types | |
| – limitations of explanations (data, features, models) | |
| – interpreting anomalies vs. model behavior | |
| – limitations in explainability | |
Interactive session: brainstorming and discussion on trust, usability, and open challenges | |
| 16:30 | End of course |
