Robust Machine Learning Hyper-parameter Optimisation using Taguchi Methods


The accuracy and generalisability of a machine learning model is determined by the right choice of hyper-parameters.  All models have hyper-parameters (even non-parametric models) and the wrong choice will result in a poor fit to the data, and useless predictions.  Finding the optimal set of hyper-parameter for any model is always the most time consuming part of machine learning, and is usually done by extensive (and sometimes exhaustive) grid searches, or highly variable random selection.  An alternative is to use Robustification, which is a type of parameter design often associated with Taguchi methods.  In this project you will implement robustification for hyper-parameter optimization of a simple machine learning model in python and compare the result (accuracy and computational time) to random and grid searching methods.


To produce a python module for a library for general use by machine learning researchers, and a scientific publication. Data sets will be provided.


Python programming and experience in data science and machine learning is essential (such as COMP3720, COMP4660, COMP4670, COMP6670, COMP8420).  Familiarity with platforms such as scikit-learn, Pytorch, Tensorflow and Keras is desirable.


This is a 24cp project.


machine learning, optimisation, data science, python

Updated:  1 June 2019/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing