Provide a solid grounding in GLM and Bayesian credibility theory.
Provide good knowledge in time series including applications.
Provide an introduction to machine learning techniques.
Demonstrate how to apply software R to solve questions
Prepare students adequately to sit for the exams in CS1 and CS2 of the Institute and Faculty of Actuaries.

(LO1) Be able to explain concepts of Bayesian statistics and calculate Bayesian estimators.

(LO2) Be able to state the assumptions of the GLM models - normal linear model, understand the properties of the exponential family.

(LO3) Be able to apply time series to various problems.

(LO4) Understand some machine learning techniques.

(LO5) Be confident in solving problems in R.

(S1) Problem solving skills

(S2) Numeracy

(a) GLM:

The exponential family of distributions for the binomial, Poisson, exponential, Gamma, normal distribution.

(b) Bayesian statistics:

Bayes' Theorem, prior and a posteriori distribution. Loss function, credibility premium formula, role of credibility factor, Bayesian approach to credibility theory, empirical Bayes approach to credibility theory and its use to derive credibility premiums in simple cases.

(c) Time series:

Serial dependence (autocorrelation, correlograms), stationary and non-stationary processes. Moving Average (MA) processes, Autoregressive (AR) processes, Autoregressive moving average processes (ARMA), extension to integrated processes (ARIMA) and non-linear processes, such as ARCH and GARCH, the multivariate autoregressive model. Concept of co-integration in time series and applications of time series models.

(d) Machine learning:

Principles of machine learning, key supervised and unsupervised machine learning t echniques, explaining the difference between regression and classification and between generative and discriminative models. Applications of machine learning techniques to simple problems.