F71RB - Machine Learning for Risk and Insurance 2

Course leader(s):

• Debora Daniela Escobar (Edinburgh, January)

Syllabus

1. Syllabus (1.1 Supervised Learning Methods , 1.2 , 1.3 The course will focus on supervised Machine Learning methods that can be applied in risk, insurance, and finance. The list of techniques below represents a wide range of supervised machine learning techniques. The course will be adapted to the applications and challenges in the insurance and finance sector. , 1.4 , 1.5 Review of linear regression and logistic regression , 1.6 , 1.7 Generalized linear models , 1.8 , 1.9 Linear model selection and regularisation , 1.10 , 1.11 Generalised Linear Models and Regularisation , 1.12 , 1.13 Generalised Additive Models , 1.14 , 1.15 Tree-based methods , 1.16 , 1.17 Kernel methods , 1.18 , 1.19 Deep Neural Networks)

Learning outcomes

By the end of the course, students should be able to do the following:

• understand core mathematical and statistical concepts in supervised machine learning, particularly in the context of risk, insurance, and financial mathematics.
• apply supervised learning methods, such as Generalized Linear Models and Regularization techniques, to solve problems in risk, insurance, and finance.
• demonstrate an understanding of the underlying mathematics of supervised machine learning techniques, including Generalized Linear Models and Regularization.
• critically evaluate the appropriateness and performance of different supervised learning techniques in risk, insurance, and financial contexts, as well as the relationships between various supervised learning methods.
• propose solutions using supervised learning methods to address identified problems in the context of risk, insurance, and finance.
• demonstrate proficiency in implementing machine learning methods using R. Python may be used.

Further details

Curriculum explorer: Click here

SCQF Level: 11

Credits: 15