Artificial Intelligence and Deep Learning Models for Actuarial Applications
Lecture slides from UNSW’s ACTL3143 & ACTL5111 courses
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.
List of Topics Covered
Lecture 1: Python
- default arguments
- dictionaries
- f-strings
- function definitions
- Google Colaboratory
help
- list
pip install ...
range
- slicing
- tuple
type
- whitespace indentation
- zero-indexing
Lecture 2: Deep Learning
- activations, activation function
- artificial neural network
- biases (in neurons)
- callbacks
- cost/loss function
- deep/shallow network, network depth
- dense or fully-connected layer
- early stopping
- epoch
- feed-forward neural network
- Keras, Tensorflow, PyTorch
- matplotlib, seaborn
- neural network architecture
- overfitting
- perceptron
- ReLU activation
- representation learning
- training/validation/test split
- universal approximation theorem
- weights (in a neuron)
Tutorial 2: Forward Pass
- batches, batch size
- forward pass of network
- gradient-based learning
- learning rate
- stochastic (mini-batch) gradient descent
Lecture 3: Tabular Data
Categorical Variables
- entity embeddings
- Input layer
- Keras functional API
- nominal variables
- ordinal variables
- Reshape layer
- skip connection
- wide & deep network
Classification
- accuracy
- confusion matrix
- cross-entropy loss
- metrics
- sigmoid activation
- sofmax activation
Lecture 4: Computer Vision
- AlexNet, GoogLeNet, Inception
- channels
- computer vision
- convolutional layer & CNN
- error analysis
- fine-tuning
- filter/kernel
- flatten layer
- ImageNet
- max pooling
- MNIST
- stride
- tensor (rank, dimension)
- transfer learning
Tutorial 4: Backpropagation
- backpropagation
- partial derivatives
Lecture 5: Natural Language Processing
- bag of words
- lemmatization
- one-hot embedding
- stop words
- vocabulary
- word embeddings/vectors
- word2vec
Lecture 6: Uncertainy Quantification
- aleatoric and epistemic uncertainty
- Bayesian neural network
- deep ensembles
- dropout
- ensemble model
- CANN
- GLM
- MDN
- mixture distribution
- Monte Carlo dropout
- posterior sampling
- proper scoring rule
- uncertainty quantification
- variational approximation
Lecture 7: Recurrent Networks and Time Series
- GRU
- LSTM
- recurrent neural networks
- SimpleRNN
Lecture 8: Generative Networks
Lecture 8-9: Generative Networks
- autoencoder (variational)
- beam search
- bias
- ChatGPT (& RLHF)
- DeepDream
- generative adversarial networks
- greedy sampling
- Hugging Face
- language model
- latent space
- neural style transfer
- softmax temperature
- stochastic sampling
Lecture 9: Interpretability
- global interpretability
- Grad-CAM
- inherent interpretability
- LIME
- local interpretability
- permutation importance
- post-hoc interpretability
- SHAP values
Contributors
- Tian (Eric) Dong
- Michael Jacinto
- Hang Nguyen
- Marcus Lautier
- Gayani Thalagoda
Copyright
UNSW Sydney.