Course co-ordinator(s): Dr Abdul-Lateef Haji-Ali (Edinburgh).
Aims:
The intention of this course is to introduce students to core mathematical and statistical components of modern machine learning methods that are directly of applicability in the risk, insurance and financial mathematics contexts. In addition, the applications presented, and Python computer packages explored in the applications will be focussed primarily on this discipline specific context.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh.
Semester: 2.
Syllabus:
Supervised Learning Methods
• Generalised Linear Models and Regularisation
• Lasso, Elastic Net, Grouped Lasso, Graphical Lasso
• Bayesian GLM and regularization priors
• PCA Regression in elliptical models
• Generalised Additive Models
• Spline models, B-Splines and tensor splines in interpolation for spatial data
• Kernel Generalised Linear Models
• Kernelized Natural Exponential Family
• Generalised Additive Models for Location Shape and Scale GAMLSS
• Properties and Estimation
• Functional Regression
• Properties, Estimatio
• Kernel Machines
• Classes of kernel functions, properties and model structures
• Support Vector Machines: Classification and Regression
• Support Measure Machines
• Gaussian Processes
• Regression
• Warped Gaussian Processes
• Constrained Gaussian Processes
• Ensemble Methods
• Combining Rules, Random Forest, Bagging, Boosting and AdaBoost
SCQF Level: 11.
Credits: 15.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the course leader.
Canvas: further information and course materials are available on Canvas