Five advanced level afternoon workshops take place over a week to give you a sound understanding of various topics related to the big data analysis techniques.
Course content
Introduction to big data
Dr Craig McNeile
A session to introduce you to big data, understand how you’re using it, think about how you might optimise it to make smarter decisions.
Gaussian process
Dr Mu Niu and Dr Zhenwen Dai
The Gaussian Process workshops are aimed at users who want to understand and use Gaussian process models, both in theory and practice. The following topics will be briefly covered: Gaussian process introduction, Gaussian process regression, Gaussian process classification and kernel design. The participants are expected to have basic knowledge about linear algebra and multi-variant Gaussian process distribution.
Machine learning
Dr Matthew Craven and Dr Malgorzata Wojtys
- Parametric methods: Linear Discriminant Analysis and Quadratic Discriminant Analysis
- Non-parametric methods: tree-based methods: Classification trees, Random forests, and Boosting: AdaBoost algorithm.
- Evolutionary Algorithms with Applications/Artificial intelligence
- Monte-Carlo method (AKA hillclimber)
- Genetic Programming, Genetic Improvement, Automated Design of Algorithms
Neural Network
Dr Craig McNeile
The basic ideas of deep learning and neural networks will be introduced. The applications will include the classification of images and the analysis of time series. There will be hands-on practical exercises using the Keras python library, which includes access
to TensorFlow.