Machine Learning

Machine learning research in the Department of Computer Science is mainly concerned with:

The theory and application of Computational Intelligence Techniques: Artificial Neural Networks (ANNs), Fuzzy and Neuro-Fuzzy Systems, Genetic Algorithms and Hybrid Intelligent Systems. The principal research contact for these areas is Vasile Palade.
Applications of Symbolic Machine Learning to real-world problems including the performance of computer systems (including the Grid), applied computer security, and other areas. Techniques used range from Inductive Logic Programming (ILP) to decision-tree techniques. The principal research contact for these areas is Steve Moyle.

Past activities, have been focused on the theory, implementation, and application of Inductive Logic Programming (ILP). Application areas included those in drug design, bioinformatics and chemoinformatics. Non-ILP research was concerned with the use of symbolic machine learning like decision-tree techniques to interesting real-world problems. The principal research for these areas was performed by past members including Ashwin Srinivasan. Aswhin's ILP system Aleph can be found here.