Machine Learning, Data Mining and High Performance Computing

Machine Learning, Data Mining and High Performance Computing are concerned with the automated analysis of large-scale data by computer, in order to extract the useful knowledge hidden in it. Using state-of-the-art Artificial Intelligence methods, this technology builds computer systems capable of learning from past experience, allowing them to adapt to new tasks, predict future developments, and provide intelligent decision support.

Applications are found in a wide range of fields including business, marketing, medicine, bioinformatics, robotics, computer vision and scientific discovery. Skilled professionals and researchers, who are able to apply Machine Learning and Data Mining technology to current problems and thereby push the limits of what computers can effectively do, are in high demand.

Objectives

This Advanced MSc course gives you a solid grounding in this exciting new technology, and equips you with the skills necessary to construct and apply Machine Learning, Data Mining and High Performance Computing tools in order to solve complex data analysis problems. After successfully completing this course, you will be able to:

• Determine and justify, given a problem, the suitability of Machine Learning and Data Mining techniques to the solution of that problem
• Select, given an application, the most appropriate learning or mining technique
• Design and implement Machine Learning, Data Mining and High Performance Computing solutions
• Discuss the limitations of current approaches to Machine Learning and Data Mining
• Demonstrate creativity by suggesting ways to improve existing techniques or developing new techniques and algorithms
• Communicate ideas and concepts clearly both orally and in writing

Projects previously undertaken by students on the Machine Learning, Data Mining and High Performance Computing course include:

• Adaptive pathfinding in a real-time strategy game
• An adaptive multi-agent system for searching the web
• Natural language understanding and information extraction
• Applying machine learning to manufacturing processes
• A hybrid symbolic-subsymbolic system for region classification in images
• Kernel-based methods in inductive logic programming
• A strongly typed evolutionary programming system
• Optimisation in optical design by means of genetic algorithms and neural nets
• Support vector machines for text and web classification