In this lecture series, we will explore topics of machine learning, from a statistical perspective. The topics selected will have direct relevance to modern data analytic methods in risk management and insurance.
Core topics to be discussed will be covered in three parts:
Part 1. Working with Complex Data Types: Topics will include
- feature extraction methods from non-standard data types,
- dealing with missingness, outliers, robust probabilistic feature extraction,
- topological feature extraction,
- dimension reduction methods (PCA, PPCA, ICA and functional versions).
Part 2. Regression models and Time series: Topics will include
- State Space models (reduced rank regressions and cointegration),
- GAMLSS and time series extensions,
- Covariance Regressions,
- Generalized count processes with attributes such as long-memory, self-excitation, stochastic intensity and dependence,
- Quantile regression and time series,
- Gaussian processes and warped Gaussian processes + Tukey processes.
Part 3. Estimation: Topics will include
- Monte Carlo methods: Gibbs, Metropolis Hastings, Hamiltonian Monte Carlo, Reimann-Manifold HMC, Geodesic HMC,
- Sequential Monte Carlo,
- Particle Markov chain Monte Carlo.
Applications will come from insurance and risk: Mortality modelling, non-life claims reserving analysis, temperature and weather modelling, commodity futures, Green finance and Green bonds, interest rate models.
Time: Wednesdays, 10:15-12
Auditorium: HG G 43
Begins: Wednesday, September 27, 2017.