In this project, the idea would be to familiarize oneself further with the following concepts

- time series data
- Wavelet analysis to generate features
- several types of machine learning models
- kernel ridge regression
- multilayer perceptron
- radial basis function networks
- transfer learning using some well-known image classifier

Application data can range from Quantum Chemistry over Finance to Health, hence is very broad.

The main objective would be to start with a “black box” approach, i.e. using some existing implementation of a continuous wavelet filter bank and then to develop a deeper understanding on how the choice of some parameters in the wavelet filter bank influences the prediction quality.

A first reference: