Artificial Intelligence
(
SET11125
)
The module is partly based on the first three sections of “Artificial Intelligence: A Modern Approach” (3rd edition) by Russell and Norvig. The indicative content from the book is as follows: Introduction: What is AI? History of AI and the state of the art. Agents : An introduction to agents, their behaviors and structure Searching: Problem solving by searching, heuristics, local search and optimisation and adversarial search Constraint satisfaction problems: defining and solving CSPs Logic: Propositional logic, first-order logic, knowledge representation In addition to this the module will feature introductions to other AI techniques including neural networks, machine learning and nature inspired methods.
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Data Analytics
(
SET11122
)
The aim of this module is to enable you to develop a deep understanding of the fundamentals of data analytics, and to give you opportunities to practise a set of popular data analytical tools. Topics covered include:*Data Pre-processing – data quality, data cleaning, data preparation*Data Analytics – techniques of analysing data, such as classification, association, clustering and visualisation, including a variety of machine learning methods that are widely used in data mining* Post processing – data visualisation, interpretation, evaluationThis module will use tools such as OpenRefine, Weka and Tableau for standard and structured data The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools and practical skills in deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.
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Data Management & Processing
(
SET11126
)
This module will explore and develop data management and processing solutions that will work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.First, modern tools and techniques in data management will be examined, with the emphasis on good practice and professional approaches of storing and handling data. Next, the module will examine ways of cleaning noisy real-world data in order to make it suitable for data processing. Finally, data processing and collation techniques such as Machine or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:• Data warehousing and storage techniques• Data cleaning techniques• A discussion of cloud approaches• Data processing and collation techniques• An introduction to parallel data pipeline approaches
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Machine Learning for Conversational AI
(
SET11124
)
Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives. The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.
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Masters Dissertation
(
SOC11101
)
The work for this module comprises the completion of an individual research project. Each student is assigned a personal Supervisor, and an Internal Examiner who monitors progress and feedback, inputs advice, examines the dissertation and takes the lead at the viva. There are two preliminary deliverables prior to the submission of the final dissertation: (1) Project proposal (2) Initial Report including time plan and dissertation outline
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Research Methods for Computer Science
(
SET11131
)
Acquiring robust research skills is key for postgraduate students, both as preparation for your dissertation and your future work and study. In this module, you will acquire the foundations of these skills. In particular, you will be guided through the following stages:• producing a systematic appraisal of the literature, identifying strengths and weaknesses of published research, as well as identifying gaps in the knowledge base;• developing a research methodology to address a gap in the knowledge base; and• producing a sound analysis of a dataset to determine whether you have adequately addressed the gap.Throughout, ethical research skills will be embedded, so that others will have confidence in the research you propose, and, ultimately, undertake.
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Scripting for Natural Language Processing
(
SET11128
)
Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives. The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.
Further information
* These are indicative only and reflect the course structure in the current academic year. Some changes may occur between now and the time that you study.