Several Members of the CLRC are part of the permanent teaching staff at the Department of Computer Science, Royal Holloway, University of London, and have been responsible for developing a number of highly successful final year undergraduate lecture courses. In addition, the Department offers an undergraduate degree programme in Computer Science with Artificial Intelligence.
Most everyday reasoning and decision making involves uncertainty: typically we do not know for sure how the piece of the environment that we are interested in will behave; at best we will only have a set of models (or hypotheses) one of which, we believe, is its satisfactory description. Inductive learning is concerned with identifying the "best", in some sense, hypothesis. This course is intended as an introduction to inductive inference and decision making systems and will give practical experience in developing such systems. Particular emphasis will be on "dual" methods, which make it possible to solve problems involving billions of parameters; such problems were believed to be infeasible until recently. Many successful applications of the methods covered in the course include image recognition, text categorisation (mainly motivated by the Internet) and face recognition.
In recent years financial industry has been transformed, and it is widely believed that the transformation will continue. A key role in this transformation is played by the advent of derivatives, financial instruments which facilitate managing financial risks. Pricing derivatives (and associated strategies of dynamic hedging) will be the main topic of this course. It is called ``computational finance'' because many derivatives are too complicated to be priced and hedged using simple mathematical formulae; advanced computational models are required. In the end of the course the students are expected to: understand mathematical and computational models of underlying and derivative securities; master techniques for pricing derivatives and for dynamic hedging; be able to apply these models and techniques for creating computer programs.
This course introduces Neural Networks, an important branch of artificial intelligence. The connectionist approach is currently used for problems such as pattern recognition, for example in application to images and sound. The principal Neural Network models currently developed will be introduced by showing how they relate to each other and to the functions of biological brains. The course includes a project giving hands on experience of the techniques involved in applying Neural Networks to practical problems.
Biology may be the Physics of the 21st century, and interpreting the large amounts of data emerging from new methods in biology, including projects to sequence the genomes of many organisms, pose many new problems which are only soluble by computer-based approaches (bioinformatics). The course will introduce the main approaches currently in use in bioinformatics, with special emphasis on the analysis of DNA and protein sequences emerging from genome sequencing projects.