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Presentations

PRESENTATIONS


Day 1 (Monday)
A Brief Overview of Probability Theory in Data Science by Geert Verdoolaege


Day 2 (Tuesday)
Session I — Uncertainty Propagation of Experimental Data in Modelling Codes

Session II — Data Analysis Lessons Learnt, Best Practices and Proposals for ITER

Session III — Regression Analysis Profiles, Scaling and Surrogate Models


Day 3 (Wednesday)
Session III — Regression Analysis Profiles, Scaling and Surrogate Models (Cont.)

Session IV — Learning in Non-Stationary Conditions for Experimental Design and Predictions

Session V — Inverse Problems

Day 4 (Thursday)
Session VI — Image Processing

Session VII — Causality Detection in Time Series

Session VIII — Synthetic Diagnostics, Integration, Verification and Validation

Day 5 (Friday)
Session IX — Deep Learning