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School of Electronic Engineering and Computer Science

Mr Andrew Lewis-Smith


Room Number: Peter Landin, CS 438


Applied Statistics (Postgraduate)

The module introduces core statistical concepts for practical data analysis. It will provide students with the skills to model data sources, analyze their statistical properties, visualize them in different ways and fit the samples to a known probabilistic model.

Computer Systems and Networks (Undergraduate)

This module provides you with a basic understanding of how a computer works and how programs are executed by the CPU at the machine level. As an introduction to computer architecture and systems software, this module presents the concepts needed to understand typical computers at the level of their ';machine-code'; instruction set. It covers Boolean algebra rules and terminology as well as logic gates. The module also examines the use of bits, bytes and data formats to represent integers, text and programs as well as looking at the conventional von Neumann computer architecture (CPU, registers, memory). Assembly language programming and system software are introduced.

Data Mining (Postgraduate)

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.

Information Retrieval (Postgraduate)

The field of information retrieval (IR) aims to provide techniques and tools to support effective and efficient access to large amounts of textual information (e.g. stored on the web, digital libraries, intranets). This module will describe the IR field in details, both its theoretical and empirical aspects. The following topics will be covered: Indexing: Representing the information content of documents through the use of e.g. stop word removal, stemming, and term weight calculation. Retrieval: Building models that select which information objects are relevant to a user''s need. Models will include Boolean model, vector space model, probabilistic model, language model, inference network model, and relevance feedback model. Evaluation: Implementing and evaluating IR models, mainly with respect to effectiveness aspects. The course consists of 3 hours per week of lectures for 12 weeks, including labs and tutorials. Labs will make use of the HySpirit, a state-of-the-art IR experimental platform to design and implement indexing and retrieval strategies.

Interaction Design (Undergraduate)

Traditionally, interactive systems design has focused on enhancing people's efficiency or productivity. For example, to increase the speed with which tasks can be completed or to minimise the number of errors people make. Economic and social changes have led to a situation in which the primary use of many technologies is for fun; ie. in which there is no quantifiable output and no clear goal other than enjoyment. Computer games, mobile music players and online communities are all examples where the quality of the experience is the primary aim of the interaction. This module explores the challenges these new technologies, and the industries they have created, present for the design and evaluation of interactive systems. It moves away from a human computer interaction model, which is too constrained for real world problems and provides you with an opportunity to engage with theories relating to cultural dynamics, social activity, and live performance. It explores the nature of engagement with interactive systems and between people when mediated by interactive systems.

Probability and Matrices (Undergraduate)

This module covers: Probability theory Counting permutations and combinations Conditional probabilities Bayesian probability Random variables and probability models Vector and matrix algebra Linear equations Vector spaces Linear combinations, linear independence


Research Interests:

Substructural Logics; Algebraic semantics and algebras in general; Proof theory.