Miss Jiali Wang
Email: firstname.lastname@example.orgRoom Number: Peter Landin, CS 437
Bayesian Decision and Risk Analysis (Postgraduate)
The module will cover: Introduction to information systems; Types of information system; Uses of Information systems; Information systems in e-commerce and e-business; Information system design and development; Case studies of business information systems; The human factor in information systems; Legal and ethical issues in Information systems.
Bayesian Decision and Risk Analysis (Undergraduate)
The role of software is increasingly critical in our everyday lives and the accompanying risks of business or safety critical systems failure can be profound. This module will provide you with a framework for articulating and managing the risks inherent in the systems you will develop as a practitioner. Likewise, you will learn how to build decision-support tools for uncertain problems in a variety of contexts (legal, medical, safety), but with a special emphasis on software development. This module will make a distinctive offering that will enable you to bring a principled approach to bear to analysing and solving uncertain and risky problems. Module contents: Quantification of risk and assessment: Bayesian Probability and Utility Theory, Bayes Theorem and Bayesian updating; Causal modelling using Bayesian networks with examples; Measurement for risk: Principles of measurement, Software metrics, Introduction to multi-criteria decision aids; Principles of risk management: The risk life-cycle, Fault trees, Hazard analysis; Building causal models in practice: Patterns, identification, model reuse and composition, Eliciting and building probability tables; Real world examples; Decision support environments.
Data Mining (Undergraduate)
Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.