Artificial Intelligence and Deep Learning Models for Actuarial Applications

Lecture slides from UNSW’s ACTL3143 & ACTL5111 courses

Author

Dr Patrick Laub

Overview

These are the lecture slides from my recent “Artificial Intelligence and Deep Learning Models for Actuarial Applications” courses (coded ACTL3143 & ACTL5111) at UNSW. They can be used to see what topics I covered in these courses. The slides are not intended to be used to learn deep learning from scratch. For that, you need to attend the lectures & complete the assessment.

Lecture Materials

Readings

The readings from the book will come mainly from Géron (2022), which is available through the UNSW Library’s access to O’Reilly Media texts. I’ll give references to the 3rd edition, but if you get your hands on a copy of the 2nd edition then that is also fine. Some readings will be from James et al. (2021) (or equivalently the the Python version James et al. (2023)) which is available online; you’ll need the 2nd edition for this (the deep learning chapter is not in the 1st edition). Note, if I say “read from A up to B”, that means to read A but stop at B (without reading it).

Week Readings
1 Géron (2022): Chapter 1 “The Machine Learning Landscape”, Chapter 2 “End-to-End Machine Learning Project” (up to “Handling Text and Categorical Attributes”), James et al. (2021): Sections 10.1 “Single Layer Neural Networks” & 10.2 “Multilayer Neural Networks”
2 Géron (2022): Chapter 2 “End-to-End Machine Learning Project” (up to “Launch, Monitor, and Maintain Your System”), Chapter 3 “Classification” (up to “Multilabel Classification”), Chapter 10 “Introduction to Artificial Neural Networks With Keras” (up to “Building Complex Models Using the Functional API”)
3 James et al. (2021): Section 10.3 “Convolutional Neural Networks”, Géron (2022): Chapter 14 “Deep Computer Vision Using Convolutional Neural Networks” (just skim through the specific historical architectures, like InceptionNet etc.)
4 James et al. (2021): Section 10.4 “Document Classification”, Vajjala et al. (2020): Chapters 1 and 2 (up to “Modeling”)
5 James et al. (2021): Section 10.5 “Recurrent Neural Networks”, Géron (2022): Chapter 15 “Processing Sequences Using RNNs and CNNs”, Hyndman & Athanasopoulos (2018): Section 5.1-5.3 and 5.8
7 Géron (2022): Chapter 11 “Training Deep Neural Networks”, Chapter 13 Section “Encoding Categorical Features Using Embeddings”
8 Schelldorfer & Wüthrich (2019)
9 Molnar (2020) Chapter 4 “Methods Overview”, Charpentier (2024) Chapter 4 “Models: Interpretability, Accuracy, and Calibration”
10 Chollet (2021): Chapter 14 “Conclusions”

The following readings are for those who are taking ACTL3142/ACTL5110 at the same time as ACTL3143/ACTL5111 (or who just need to brush up on that course a little):

Week Readings (ACTL3142 Revision)
1 James et al. (2021): Chapter 2, Sections 3.1, 3.2, and 5.1.1
2 James et al. (2021): Section 3.3.1, 4.1, 4.2, 4.3

Other useful resources include the Actuaries Institute’s Actuaries’ Analytical Cookbook and the Swiss Association of Actuaries’ Actuarial Data Science Tutorials.

Contributors

  • Tian (Eric) Dong
  • Michael Jacinto
  • Marcus Lautier
  • Sam Luo
  • Hang Nguyen
  • Gayani Thalagoda

References

Charpentier, A. (2024). Insurance, biases, discrimination and fairness. Springer.
Chollet, F. (2021). Deep learning with Python. Simon and Schuster.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly Media.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.
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.
Molnar, C. (2020). Interpretable machine learning.
Schelldorfer, J., & Wüthrich, M. V. (2019). Nesting classical actuarial models into neural networks. Available at SSRN 3320525.
Vajjala, S., Majumder, B., Gupta, A., & Surana, H. (2020). Practical natural language processing: a comprehensive guide to building real-world NLP systems. O’Reilly Media.