Statistical Machine Learning for Risk and Actuarial Applications

Lecture slides for UNSW’s ACTL3142 & ACTL5110 courses

Overview

These are the lecture slides from the “Statistical Machine Learning for Risk and Actuarial Applications” courses (coded ACTL3142 & ACTL5110) at UNSW. They can be used to see what topics are covered in these courses. You won’t be able to just read these materials to become accomplished in these topics. For that, you need to attend the lectures & complete the assessment.

Lecture Materials

• Course Overview (slides)
• Introduction To Statistical Learning (slides, annotated-3142, annotated-5110)
• Linear Regression (slides, annotated-3142, annotated-5110)
• Logistic Regression (slides, annotated-3142, annotated-5110)
• Generalised Linear Models (slides, Data PrivateCarIns1975-Data.csv, annotated-3142, annotated-5110)
• Machine Learning: Cross-Validation and Regularisation (slides)
• Moving Beyond Linearity (slides, splines interactive demo)
• Tree-based Methods (slides)
• Unsupervised Learning (slides)
• Exam Topics (slides)

Labs

• Lab 1: Introduction To Statistical Learning (pdf)
• Lab 2: Linear Regression I (pdf)
• Lab 3: Linear Regression II (pdf)
• Lab 4: Logistic Regression (pdf)
• Lab 5: Generalised Linear Models (pdf)
• Lab 6: Cross-validation & Regularisation (pdf)
• Lab 7: Moving Beyond Linearity (pdf)
• Lab 8: Tree-based Methods (pdf)
• Lab 9: Clustering (pdf)
• Lab 10: Principal Component Analysis (pdf)

R functions

The labs involve applied questions which can be solved using R. Here is a short glossary of the relevant R functions.

Readings

The readings from the book will come mainly from:

• James et al. (2021), An Introduction to Statistical Learning with Applications in R or
• James et al. (2023), An Introduction to Statistical Learning with Applications in Python and
• De Jong & Heller (2008), Generalized Linear Models for Insurance Data (available via UNSW Library website)

A fantastic source for optional readings, Géron (2022), is also available through the UNSW Library’s access to O’Reilly Media texts.

Week	Required	Optional
1	James et al. (2021): Chapter 1, Chapter 2, Chapter 3 up to and including 3.1.1 Estimating the coefficients	-
2	James et al. (2021): Rest of Chapter 3, Chapter 6.1	-
3	James et al. (2021): Chapters 4.1, 4.2, 4.3, 4.6, 4.7.1, 4.7.2, 4.7.6, 4.7.7	De Jong & Heller (2008): Chapter 4
4	De Jong & Heller (2008): Chapters 5.1, 5.2, 5.3, 5.4	De Jong & Heller (2008): Chapter 5.5
5	De Jong & Heller (2008): Chapters 5.6, 5.7, 5.9, 5.11	De Jong & Heller (2008): Chapter 5.10; Haberman & Renshaw (1996)
7	James et al. (2021): Chapter 5 (skipping bootstrap sections), and Section 6.2 (skipping Bayesian interpretation).	-
8	James et al. (2021): Chapter 7	-
9	James et al. (2021): Chapter 8 (skipping Bayesian Additive Regression Trees)	Géron (2022): Chapters 6 & 7
10	James et al. (2021): Chapter 12 (skipping missing values & matrix completion)	Géron (2022): Chapters 8 & 9

Annotated Slides (2024 T1)

• Introduction To Statistical Learning
• Linear Regression
• Logistic Regression
• Generalised Linear Models
• Machine Learning Ideas
• Moving Beyond Linearity (no annotations)
• Tree-based Methods
• Unsupervised Learning

Contributors

Many lecturers and TAs contributed to these materials over the last years. The following list is in reverse chronological order of the most recent time someone has contributed.

• Dr Patrick Laub
• Dr Patrick Wong
• Jovana Kolar
• A/Prof. Andrés Villegas
• Dr Fei Huang
• Dr Héloïse Labit Hardy
• Dr Mengyi Xu
• Prof. Bernard Wong

License

This repository includes references from other open books, each subject to their respective licenses. All materials created by me are licensed under the Creative Commons - NonCommercial 4.0 International license.

Copyright

UNSW Sydney.

References

De Jong, P., & Heller, G. Z. (2008). Generalized linear models for insurance data. Cambridge University Press.

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly Media.

Haberman, S., & Renshaw, A. E. (1996). Generalized linear models and actuarial science. Journal of the Royal Statistical Society: Series D (The Statistician), 45(4), 407–436.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R. Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: with Applications in Python. Springer.