Knowledge Technologies

In this course, students will learn algorithms and data structures for extracting, retrieving and analysing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and markup, web crawling, regular expressions, document indexing, text retrieval, clustering, classification and prediction, pattern mining, and approaches to evaluation of knowledge technologies.

Declarative Programming

This course presents declarative programming languages and techniques. Declarative programming languages provide elegant and powerful programming paradigms which every programmer should know.

Distributed Systems

The course aims to provide an understanding of the principles on which the Web, Email, DNS and other interesting distributed systems are based. Questions concerning distributed architecture, concepts and design; and how these meet the demands of contemporary distributed applications will be addressed.

Research Methods

This course is an introduction to the processes of science as they apply to computing and related disciplines, including designing experiments, locating relevant literature, writing papers, giving presentations and refereeing. Underlying all of these, course will foster the development of critical thinking, a skeptical, scientific perspective, and scientific ethics.

Mobile Computing Systems Programming

This course will cover fundamental mobile computing techniques and technologies, and explain challenges that are unique to mobile computing. In particular, the development of software for mobile devices requires hands-on experience that cannot be captured using simulation environments or emulators. Mobile device have limited computing power and restrictions on the communication bandwidth, latency and network availability.

Advanced Database Systems

The course will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The course will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

Statistical Machine Learning

This course is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems. Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

Programme Analysis and Transformation

This course is concerned with meta-programmes - programmes that work on other programmes, possibly generating programmes as output. People routinely read, generate, analyse, test, and transform programmes. For example, a programmer may look through code for potential buffer overruns, and may add runtime tests to avoid the security problems that could result. It is preferable, however, to automate such activity as far as we can, partly because that makes programmers more productive, and partly because computers generally are better at these tasks, avoiding human oversights and mistakes. This course introduces the main techniques and applications of programme analysis and transformation, including methods used by modern optimizing compilers and allied tools.

AI Planning for Autonomy

Programming Language Implementation

This course provides an understanding of the main principles of programming language implementation, as well as first hand experience of the application of those principles. The course describes how compilers analyse source programmes, how they translate them to target programmes, and what tools are available to support these tasks. Topics covered include compiler structures; lexical analysis; syntax analysis; semantic analysis; intermediate representations of programmes; code generation; and optimisation.

Web Search and Text Analysis

This course is for students to develop an understanding of the main algorithms used in natural language processing and text retrieval, for use in a diverse range of applications including search engines, cross-language information retrieval, machine translation, text mining, question answering, summarisation, and grammar correction. Topics to be covered include text normalisation, sentence boundary detection, part-of-speech tagging, n-gram language modelling, and text classification. The programming language used is Python

Thinking and Reasoning with data

This course will address these questions by teaching students critical thinking and data analysis skills. After completing this subject students will understand the basic principles of sampling and experimental design, how the results of statistical analyses are reported, the statistical thinking behind common statistical procedures and will be able to carry out a range of standard statistical techniques.

Systems Modelling and Simulation

This course is aimed to present that modern science and business makes extensive use of computers for simulation, because complex real-world systems often cannot be analysed exactly, but can be simulated. Using simulation we can perform virtual experiments with the system, to see how it responds when we change parameters, which thus allows us to optimise its performance. We use the language R, which is one of the most popular modern languages for data analysis.

Science Communication

Students will develop skills in evaluating examples of science and technology communication to identify those that are most effective and engaging. Students will also be given multiple opportunities to receive feedback and improve their own written and oral communication skills. Students will work in small teams on team projects to further the communication skills developed during the seminar programme. These projects will focus on communicating a given scientific topic to a particular audience using spoken, visual, written or web-based communication.

Statistics for research workers

This course is designed to provide students with detailed training in statistical methods as applied to the design and analysis of projects undertaken by postgraduate students, across all disciplines.

Science in School

This course will provide an understanding of student's university studies within Zambian schools through a substantial school based experience. The course includes a placement of up to 20 hours within a Zambian school classroom, offering an opportunity to collaborate as a Student Teacher (ST) under the guidance of a qualified teacher.