Probability theory
- Introduction to probability theory
- Bayesian and frequentist approaches to probability
- Random variables, distributions and main properties
Statistical inference
- Parameter estimates, properties of estimators
- Maximum likelihood method
- Pearson and Neyman chi-squares
Hypothesis testing and interval estimation
- Hypothesis testing
- Goodness of fit
- Frequentist and Bayesian upper limits
Multivariate analysis
- Complex network analysis
- Multivariate discrimination methods
- Boosted decision trees
- Artificial neural networks
- Deep Learning
Statistical software tools
- Overview of the main statistical tools
- RooFit, RooStats
- Usage examples and code demonstrations
Confirmed lecturers:
- Roger Barlow (Univ. of Huddersfield)
- Olaf Behnke (DESY, Germany)
- Glen Cowan (Royal Holloway, London)
- Ilya Narsky (MathWorks, USA)
- Mario Pelliccioni (INFN Torino)
- Antonio Scala (CNR, Rome)
- Andrey Ustyuzhanin (Yandex, Russia)
