# Unsupervised Learning Working Party - Tutorials

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 | What is Machine Learning? | Background to Machine Learning | |

2 | Unsupervised background | An overview of what Unsupervised Learning is about | |

3 | Comparison of learning methods | A brief comparison between supervised vs unsupervised vs semi-supervised vs reinforced techniques | |

4 | Real world applications | Examples of where machine learning is used in the real world | |

5 | Actuarial applications | Examples of current and potential use in the actuarial industry |

## 02: Overview of Unsupervised Approaches

Entry | Link | Description | |
---|---|---|---|

1 | Clustering | Overview of clustering techniques and applications | |

2 | Association rules | Overview of association rules techniques and applications | |

3 | 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 | Principal Component Analysis | Overview of Principal Component Analysis | |

2 | Kernel Principal Component Analysis | Overview of Kernel Principal Component Analysis | |

3 | Non-Negative Matrix Factorization | Overview of Non-Negative Matrix Factorization | |

4 | Singular Value Decomposition | Overview of Singular Value Decomposition | |

5 | t-Distributed Stochastic Neighbour Embedding | Overview of t-Distributed Stochastic Neighbour Embedding | |

6 | Spectral Embedding | Overview of Spectral Embedding | |

7 | Locally Linear Embedding | Overview of Locally Linear Embedding | |

8 | Linear Discriminant Analysis | Overview of Linear Discriminant Analysis |

## 06: Neural networks

Entry | Link | Description | |
---|---|---|---|

1 | Neural networks | Introduction to neural networks | |

2 | Perceptron | Overview of perceptrons | |

3 | Feed forward neural network | Overview of feed-forward neural networks | |

4 | Multilayer perceptron | Overview of multi-layer perceptrons | |

5 | Convolutional Neural Network | Overview of convolutional neural networks | |

6 | Radial Basis Functional Neural Network | Overview of radial basis functional neural networks | |

7 | Recurrent Neural Network | Overview of recurrent neural network | |

8 | LTSM | Overview of “long-term short memory” | |

9 | Sequence to Sequence Models | Overview of sequence-to-sequence models | |

10 | Modular neural network | Overview of modular neural network |

## 07: Unsupervised networks

Entry | Link | Description | |
---|---|---|---|

1 | Hopfield | Overview of Hopfield networks | |

2 | Boltzmann | Overview of Boltzmann machines | |

3 | RBM | Overview of restricted Boltzmann machines | |

4 | Stacked Boltzmann | Overview of stacked Boltzmann machines | |

5 | Helmholtz | Overview of Helmholtz machines | |

6 | Autoencoders | Overview of Autoencoders |

## 08: Coding

Entry | Link | Description | |
---|---|---|---|

1 | Python | Introduction to coding in Python | |

2 | Numpy | Introduction to Numpy library | |

3 | Pandas | Introduction to Pandas library | |

4 | Tensorflow | Introduction to Tensorflow library | |

5 | Keras | Introduction to Keras library | |

6 | SciKit | Introduction to SciKit library | |

7 | IDEs | Introduction to exmaple IDEs e.g. Colab, Visual Studio Code |