Computer Science (Artificial Intelligence) with an Industrial Placement - MSc

Students are presented during lectures with advanced Artificial Intelligence/Machine Learning techniques (such as genetic algorithms, support vector machines (SVMs), deep learning, neural networks, stochastic gradient decent, Q-Learning/Deep Q-learning, ensembles, neuroevolution), including aspects of implementation, hyper parameter tuning, scalability and parallelism.

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This module covers the design and implementation of high-quality software, and provides an introduction to software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including effective graphical representations.

Throughout the module, students will learn to embed data analysis and statistics concepts into a programming language which offers good support for AI (e.g., Python). Students will learn to use important AI-purposed libraries and tools, and apply these techniques to data loading, processing, manipulation and visualisation.

Find out more about COMP8270

This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a data mining tool, and learn to evaluate the quality of discovered knowledge.

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There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. Similarly, there is now also an increasing interest in understanding how biological, chemical and other natural systems compute, and how this could be exploited for practical applications. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their later careers.

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Data types: nominal, numerical, ordinal, text, audio, visual, temporal and non-temporal. Basic descriptive statistics: measures of average and spread, different ways of graphing data. Choosing appropriate and valid methods for the analysis and presentation of data, and understanding the limitations of methods. Data at different scales, including big data, and the computational challenges of processing data at scale. The process of discovering useful knowledge from data: including understanding the need for preprocessing and cleaning data, the challenges of gathering relevant data, and the need to present results in a comprehensible and actionable way. Data mining: classification/regression and clustering, and the idea of predictive analytics. Elements of information retrieval from text. Vector representations of text documents. Fairness and ethical issues concerning data.

Find out more about COMP8481

This module covers the fundamental components (hardware and software) of a typical computer system, and how they collaborate to execute programs. The module provides a comprehensive overview, from the lowest level of abstractions in hardware to the highest level of abstractions of modern programming languages. Examples of topics that may be covered include logic circuits, machine language, processor organization, memory management, processes and concurrency, file systems. Throughout, special attention is paid to abstraction, performance, and other quality requirements.

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This module provides an introduction to human-computer interaction, user experience and a range of UX practices (UX - user experience - the study and practice of how people, individually and in groups, experience technologies and other artefacts, and interact with and through them.)

Fundamental aspects of human physiology and psychology are introduced and key features of interaction and common interaction styles delineated. A variety of design methods and UX practices are introduced (e.g. task-based usability testing, think-aloud protocols, first-use experiences, eye-tracking and post-session questionnaires). Throughout the course, the need for a professional, integrated and user-centred approach to interface development and evaluation is emphasised: rapid and low-fidelity prototyping feature as one aspect of this.

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The module aim is to give students an overview and understanding of key theoretical, practical and philosophical research and issues around computational creativity, and to give them practical experience in writing and evaluating creative software.

The following is an indicative list of topics that may be covered:

• Introduction to computational creativity

• Examples of computational creativity software e.g. musical systems, artistic systems, linguistic systems, proof generator systems, systems for 2D and 3D design.

• Evaluation of computational creativity systems (both of the quality and the creativity of systems)

• Philosophical issues concerning creativity in computers

• Comparison of computer creativity to human creativity

• Collaborative creativity between humans and computers

• Overview of recent research directions/results in computational creativity

• Practical experience in writing creative software.

Find out more about COMP6590

This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. Students will learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.

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In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also familiarise yourself with cognitive neural networks using state of the art simulation technology and apply these networks to the solution of problems. In addition, the module discusses examples of computation applied to neurobiology and cognitive psychology. The module also introduces artificial neural networks from the machine learning perspective. You will study the existing machine learning implementations of neural networks, and you will also engage in implementation of algorithms and procedures relevant to neural networks.

Find out more about COMP8360

This module looks into the training of modern deep neural networks: backpropagation, regularisation, automatic differentiation, computational graphs. Introduces different types of deep neural networks, such as, LSTM, convolutional networks, and autoencoders. Presents the theoretical underpinnings of deep learning and its mechanisms. Delves into selected recent advanced topics in deep learning. Examines applications of deep learning.

Find out more about COMP8685

This module provides an introduction to object-oriented programming using the popular Java language. It is designed for beginners who have not studied computer programming before. By the end students will be able to develop simple programmes using Java.

Find out more about COMP8810

This module aims to strengthen the foundational programming-in-the-small abilities of students via a strong, practical problem-solving focus. Specific topics will include introductory algorithms, algorithm correctness, and algorithm runtime. Essential data structures and algorithmic programming skills will be covered, for example, arrays, lists and trees, searching and sorting, recursion, and divide and conquer. One part of the module will also introduce students to logical formalisms such as propositional and predicate logic and some of their applications in program development.

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• Software processes.

• Modelling techniques, and the use of these techniques throughout the project lifecycle.

• Introduction to modelling principles (decomposition, abstraction, generalization,

projection/views) and types of models (information, behavioural, structural, domain and

functional).

• Risk and risk management in software.

• Approaches to software testing and inspection.

• Approaches to software configuration management.

• Security and privacy in software engineering

• Software engineering tools: configuration control, project management, integrated

development environments and modelling tools.

Find out more about COMP8860

Web-based information systems form the heart of e-commerce. They are also increasingly the way businesses handle all their information needs. Building such systems requires an understanding of up-to-date tools and technologies such as web page creation, client side programing, server side programming and databases; it also calls for an understanding of how to design systems that genuinely meet user and business needs.

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The focus of the module is on the development of the advanced English language competence necessary for post graduate study in scientific disciplines. This includes the ability to interpret and evaluate authentic scientific texts; analyse, discuss and summarise written and visual information both in writing and orally; organise written texts effectively and submit them in grammatically accurate English, and present the results of research orally in a coherent and stimulating way.

Find out more about ENLA6001

The project consists primarily of an extended period during which students undertake a substantial piece of work and a report on this in the form of a dissertation. It is preceded by an exploratory stage in which students review and summarise relevant literature or other technical background, including in a verbal presentation, and gain specific skills relevant to their project. It may be permitted to undertake the work in groups, particularly for projects with a development focus. However, the dissertations are produced individually. The project examines the student's ability to research technical background, to understand and expand on a specific problem commensurate with their programme of study and relate it to other work, to carry out investigations and development (as appropriate), to describe results and draw conclusions from them, and to write a coherent and well organised dissertation demonstrating the student's individual reflection and achieved learning.

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Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team. This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.

Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on satisfactory achievement in their academic studies.

Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.

Find out more about COMP9020

Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.

Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.

Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.

Find out more about COMP9150

Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.

Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.

Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.

Find out more about COMP9160

Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.

Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.

Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.

Find out more about COMP9170

Students spend a period working in an industrial, commercial, public sector or similar setting, applying and enhancing the skills and techniques they have developed and studied in the earlier during their MSc programme. The work is undertaken under the direction of their industrial supervisor, but support is provided by the CEMS Employability and Placements Team . This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.

Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the CEMS Employability and Placements Team. It is also dependent on students progressing satisfactorily in their studies.

Students who do not obtain a placement will be required to transfer to the appropriate course without an Industrial Placement.

Find out more about COMP9180