Miss Mariana Neves
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.
Risk and Decision-Making for Data Science and AI (Postgraduate)
This module provides a comprehensive overview of the challenges of risk assessment, prediction and decision-making covering public health and medicine, the law, government strategy, transport safety and consumer protection. Students will learn how to see through much of the confusion spoken about risk in public discourse, and will be provided with methods and tools for improved risk assessment that can be directly applied for personal, group, and strategic decision-making. The module also directly addresses the limitations of big data and machine learning for solving decision and risk problems.
Statistics for Artificial Intelligence and Data Science (Postgraduate)
This module has two components. The first introduces students to the use of probability and statistics in the context of data analysis. The module starts with basics of descriptive statistics and probability distributions. Then we go on with applied statistics techniques, such as visualisation, fitting probability distributions, time-series analysis, and hypothesis testing, which are all fundamental to the exploration, insight extraction, and modelling activities that are fundamental in handling data, of any size. The second covers some basic matrix algebra, including matrix multiplication and diagonalisation.