Since 1998 the Machine Learning and Data Mining degree has been teaching the use of state-of-the-art Artificial Intelligence (AI) methods to build computer systems capable of learning, reasoning, predicting, creating, discovering and optimising. The degree covers a very broad range of technologies and application areas, teaches both theory and practical hands-on techniques and can lead to either a PhD and academic career or a career in industry. It is designed for students with a science or engineering background who are already competent programmers and leads to an award of an Advanced MSc in Computer Science.
The degree covers or touches on many fields of study, some of which we survey below. Similarly, these fields have an enormous range of applications including both supporting human decision-making and acting autonomously. Autonomous agents range from physically embodied robots to be found increasingly in homes and factories, to virtual software agents, whether opponents in games or assistants in the on-line world. With the booming on-line economy, and a computer games industry worth more than Hollywood, we have only begun to explore the potential of these agents. Major companies such as Google© are based on intelligent search technology, while others such as social networking sites constantly generate new applications and new challenges for this kind of technology.
Data Mining is concerned with the automated analysis of large scale data in order to extract the useful knowledge hidden in it. Our approach to dealing with data has essentially remained unchanged for the past 25 centuries: we categorise it, divide it into small chunks, then build indexes and catalogues so we can find what we want. But with terabytes of data becoming available every second in areas such as finance, medicine and commerce, this is no longer a viable strategy.
As a result there is an urgent need to find new ways of solving the following types of problems. In finance, methods are needed to predict stock trends, based not only on traditional statistical indicators but, potentially, incorporating all the information that a human trader might use such as social and political trends and current events. Financial institutions need to generate credit scores before making loans, whether to individual home buyers or major corporations. Retailers need to mine associations from store card and online transaction data in order to customise advertising and promotions to customers. Banks, companies, universities, governments and other organisations are increasingly concerned with online security and the need to detect intrusions and anomalies.
As another example, with massively expanding biological data coming from new high-throughput experimental techniques, computational analysis has become a central component of molecular biology. Furthermore, since the experimental technology is arguably advancing faster than our theoretical understanding of the data being produced, there is a growing demand for people who are able to extract novel information on targets ranging from practical drug discovery to pure science. Indeed, a new discipline, bioinformatics, has emerged at the intersection of computing and biology. The analysis of genomic data requires powerful new approaches to pattern discovery, rooted both in algorithmics and statistics, and has the potential of changing the way we do medicine.
Cognitive modelling and computational neuroscience seek to unlock the secrets of the human brain by building biologically plausible models of everything from individual neurons to high-level memory and decision-making processes to, ultimately, the experience of consciousness.
However, biology is not just an application area for computer science: nature has proved a rich source of inspiration for AI. Genetic algorithms and neural networks are the best known biologically-inspired computing paradigms but there are many others ranging from DNA computing to swarm and ant-colony algorithms.
Finally, in addition to applications in science, engineering and commerce, AI methods are also being used to create music and visual art, both interactively and autonomously. As this is a young and fast-moving area, new applications of AI methods are still waiting to be discovered and the next big thing may be just around the corner.
Some other universities offer similar degrees and as each has its own focus it is worth comparing them to your own interests. Our degree has a more scientific than engineering flavour and is ideal preparation for a PhD, but it is also excellent preparation for the non academic jobs which most of our graduates move into.
What sets our degree apart is the breadth and quality of our teaching, which is linked to cutting-edge research by one of the largest AI groups in the UK, and the prestige associated with a degree from Bristol: the degree is hosted by the highly-rated Department of Computer Science and Bristol is recognised as one of the UK's top Universities.
Few areas of human endeavour will not be touched by these technologies in the 21st century. This degree will give you an excellent foundation in a young, exciting, fast-developing and richly interdisciplinary area with deep roots which build on logic, mathematics, statistics, psychology, linguistics, cognitive science, economics, operations research, neuroscience, and philosophy, among others. Perhaps the ultimate question is whether machines can achieve human-like intelligence. What do you think?
This Advanced MSc course gives you both a solid theoretical grounding and practical skills to work in this exciting area. After successfully completing this course, you will be able to: