Unsupervised Learning Working Party - Tutorials

3 minute read

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Please find below a list of tutorials which cover topics on Unsupervised Learning teachniques, including technical background and coding approaches.

01: Unsupervised Learning - Background

Entry   Link Description
1 :computer: What is Machine Learning? Background to Machine Learning
2 :computer: Unsupervised background An overview of what Unsupervised Learning is about
3 :computer: Comparison of learning methods A brief comparison between supervised vs unsupervised vs semi-supervised vs reinforced techniques
4 :computer: Real world applications Examples of where machine learning is used in the real world
5 :computer: Actuarial applications Examples of current and potential use in the actuarial industry

02: Overview of Unsupervised Approaches

Entry   Link Description
1 :bar_chart: Clustering Overview of clustering techniques and applications
2 :bar_chart: Association rules Overview of association rules techniques and applications
3 :bar_chart: Dimensionality reduction Overview of dimensionality reduction techniques and applications

03: Unsupervised Approaches Revisited - Clustering

04: Unsupervised Approaches Revisited - Association Rules

05: Unsupervised Approaches Revisited - Dimensional Reduction

Entry   Link Description
1 :unlock: Principal Component Analysis Overview of Principal Component Analysis
2 :unlock: Kernel Principal Component Analysis Overview of Kernel Principal Component Analysis
3 :unlock: Non-Negative Matrix Factorization Overview of Non-Negative Matrix Factorization
4 :unlock: Singular Value Decomposition Overview of Singular Value Decomposition
5 :unlock: t-Distributed Stochastic Neighbour Embedding Overview of t-Distributed Stochastic Neighbour Embedding
6 :unlock: Spectral Embedding Overview of Spectral Embedding
7 :unlock: Locally Linear Embedding Overview of Locally Linear Embedding
8 :unlock: Linear Discriminant Analysis Overview of Linear Discriminant Analysis

06: Neural networks

Entry   Link Description
1 :fireworks: Neural networks Introduction to neural networks
2 :fireworks: Perceptron Overview of perceptrons
3 :fireworks: Feed forward neural network Overview of feed-forward neural networks
4 :fireworks: Multilayer perceptron Overview of multi-layer perceptrons
5 :fireworks: Convolutional Neural Network Overview of convolutional neural networks
6 :fireworks: Radial Basis Functional Neural Network Overview of radial basis functional neural networks
7 :fireworks: Recurrent Neural Network Overview of recurrent neural network
8 :fireworks: LTSM Overview of “long-term short memory”
9 :fireworks: Sequence to Sequence Models Overview of sequence-to-sequence models
10 :fireworks: Modular neural network Overview of modular neural network

07: Unsupervised networks

Entry   Link Description
1 :microscope: Hopfield Overview of Hopfield networks
2 :microscope: Boltzmann Overview of Boltzmann machines
3 :microscope: RBM Overview of restricted Boltzmann machines
4 :microscope: Stacked Boltzmann Overview of stacked Boltzmann machines
5 :microscope: Helmholtz Overview of Helmholtz machines
6 :microscope: Autoencoders Overview of Autoencoders

08: Coding

Entry   Link Description
1 :milky_way: Python Introduction to coding in Python
2 :milky_way: Numpy Introduction to Numpy library
3 :milky_way: Pandas Introduction to Pandas library
4 :milky_way: Tensorflow Introduction to Tensorflow library
5 :milky_way: Keras Introduction to Keras library
6 :milky_way: SciKit Introduction to SciKit library
7 :milky_way: IDEs Introduction to exmaple IDEs e.g. Colab, Visual Studio Code