Probability theory
- Introduction to probability theory (axioms, Pascal and birthday problems), random variables, probability functions (mass and density functions), probability distributions, moments (mean, variance), correlation, covariance, and independence
- Distribution of functions of random variables (mostly sums)
- Conditional probability, Bayes theorem, representation theorem, derivation of binomial distribution, derivation of Poisson distribution from binomial and from a birth process
Statistical inference
- Parameter estimates, properties of estimators
- Maximum likelihood method
- Pearson and Neyman chi-squares
Hypothesis testing and interval estimation
- hypothesis tests
- asymptotic formulae for upper limits and significance evaluation
- treatment of nuisance parameters
- the look-elsewhere effect
Statistical software tools
- Overview of the main statistical tools
- RooFit, RooStats
- Hands-on exercises
Multivariate analysis
- Introduction to multivariate analysis
- Supervised learning: classification and regression
- The bias-variance decomposition
- Optimism, information criteria and cross-validation
- The modelling process: exploratory data analysis, feature engineering and model tuning
Machine learning
- Artificial Neural Networks
- Deep Learning
- Hands-on session: hackaton
Lecturers:
- Glen Cowan, Royal Holloway, University of London
- Sergei Gleyzer, University of Florida
- Eilam Gross, Weizmann Institute of Science
- Mario Pelliccioni, INFN Torino
- Harrison Prosper, Florida State University, Tallahassee
- Aldo Solari, University of Milano-Bicocca
