Artificial Intelligence - MSc

Year 1 modules include:

This module introduces the essential principles, methods and techniques in AI. It covers a broad range of topics such as search, planning, logic, knowledge representation and machine learning. It discusses examples of intelligent systems and studies how to develop intelligent applications such as expert systems, learning systems, and autonomous mobile and robotic systems. Students will be offered lectures, which introduces the important concepts, explain the principles and techniques, and demonstrate how to apply them to solve problems in the related topics. The workshops will provide practical sessions to help students understand the content of the lectures and build the necessary skills to develop intelligent systems.

• spring semester - Thursday afternoon

This module provides students with an in-depth appreciation of the Internet of Things (IoT) and Cloud Computing concepts, models, infrastructures and capabilities. The module will place emphasis on modern system architecture and design, Autonomous Intelligent Systems (AIS), key wireless/mobile/sensor technologies, and issues of privacy and trust, in the development of Cloud-based IoT systems. Practical work within the module will provide students with real, hands-on, experience of building a basic Internet of Things infrastructure that can access Cloud Computing services and the opportunity to develop their Python programming skills and abilities. Some basic knowledge of Python will be used throughout. Understanding of various Intelligent, wired and wireless technologies could be an advantage.

• autumn semester - Thursday morning

The module aims to strengthen students’ skills in data technologies ranging from database and data warehousing to Big Data. First, it will provide students with good understanding of database concepts and database management systems in reference to modern enterprise-level database development. Once gaining good skills in database development, students will be able to study and gain an in-depth understanding of data warehousing which include concepts and analytical foundations as well as data warehousing development. Through intensive hands-on sessions, the students will be able to get familiar with related technological trends and development in the field. the module will leverage a portfolio of SQL server tools that include SQL Server DBMS, SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS) and SQL Server Analysis Service (SSAS) to provide hands-on experience in implementing a reporting solution through a combination of assignments and lab exercises.

The module introduces also the foundation of Big data management based on Apache Hadoop platform and provides students with a broad introduction to Big Data technologies. This will involve hands-on sessions, designed for data analysts, business intelligence specialists, developers, administrators or anyone who has a desire to learn how to process and manage massive and complex data to infer knowledge from data. Topics include Hadoop, HDFS, MapReduce, Spark, Sqoop, Hive, Pig and MLlib.

• spring semester - Thursday morning

Aims of the module: what key skills and knowledge will it enable students to develop?

The module is concerned with the study and application of tools and techniques that enable the protection of information and other resources of enterprise information systems. Increases in storage, manipulation, and transfer of data across computer networks requires effective encryption techniques. This module will provide insight into some of those techniques, algorithms and their development through history. Part of the course is dedicated to the mathematics (number theory, finite fields and elliptic curves) relevant to cryptography with techniques developed using software such as Maple. The focus will also be on the analysis, design and implementation of tools and techniques that achieve the three goals of confidentiality, integrity and authenticity in security computing. Particular focus will be on the management framework that facilitate the accomplishment of the above three goals. Importantly the module will address the ethical framework of information security, the issues around privacy and data protection and the rights of private citizens to access strong encryption. Throughout the module connections with other aspects of artificial intelligence and cybersecurity will be emphasised through the examples and case studies chosen.

• autumn semester - Wednesday afternoon
• spring semester - Wednesday afternoon
• summer studies - Wednesday afternoon

The module provides students with the experience of planning and bringing to fruition a major piece of individual work. Also, the module aims to encourage and reward individual inventiveness and application of effort through working on research or company/local government projects. The project is an exercise that may take a variety of forms depending on the nature of the project and the subject area. Particular students will be encouraged to carry out their projects for local companies or government departments.
Semester: Autumn/Spring/Summer
Prerequisites: all course specific core modules
Assessment: 100% coursework (project viva is compulsory for all students)

The module aims to encourage and reward individual inventiveness and application of effort. It also aims to allow students:

• To have an opportunity to assimilate the knowledge they gained in their course and extend this knowledge to new area of application.
• To apply newly acquired knowledge and techniques to a specific problem using established research techniques and methods.
• To determine the framework of the project according to a set of specifications relevant to the subject of study.
• To manage an extended piece of work by confining the problem within the constraints of time and available resources.
• To research effectively the background material on the topic using a variety of sources and to develop ability to conduct critical analysis and draw conclusions.
• To develop the ability to produce detailed specifications and design frameworks relevant to the problem of investigation in the subject related to the industry.
• To demonstrate the originality in the application of new knowledge and skills.
• To effectively communicate the work to others by means of verbal and appropriate documentation techniques.
• To raise awareness in potential business development opportunities in an area pertinent to the topic.

• autumn semester - Wednesday morning

This module provides a comprehensive overview of the methods for machine learning and data analytics suitable for use in the data analysis and Big Data Analytics. It also provides practical skills for working with various tools for data analysis and Big Data Analytics inside and outside the platform such as Python, R, Spark, etc. The knowledge and skills obtained can be used in many tasks where the data analysis is of crucial importance for the competitiveness and the effectiveness of the businesses – customer profiling, product recommendations, market trends analysis, cybersecurity, etc. Some basic programming skills using languages for numerical processing such as Python or R or other relevant languages can be an advantage.

• spring semester - Monday evening

This module provides the theoretical foundations, the technologies and the corresponding tools for constructing intelligent model-driven software systems with explicit representation of knowledge. It will enable the students to model, design and implement software systems, which demonstrate “artificial intelligence” similar to the intelligence of the human behaviour. At the same time, it will help the students understand better the rationality behind the human intelligence.

The module follows one of the two main methodologies for dealing with the phenomena of Artificial Intelligence in computer science, known as “semiotic”, “symbolic” or “logical” paradigm. In this approach, the intelligent behaviour is achieved by incorporating common sense, heuristic and expert rules of behaviour which control the programmed algorithms for information processing during their execution in real time. For this purpose the module introduces a number of formal languages, such as FOL, DL and HCL, used for modelling the rational behaviour by logical methods, the corresponding mark-up languages, such as RDF, OWL and SWRL, which provide the necessary technology for representing the logical models in XML format alongside the respective software tools.

The module relies on some basic knowledge of discrete mathematics and formal logics typically taught in most of the undergraduate courses in engineering and science. Although it does not require extensive programming experience beyond the first introductory course in programing, working skills in programming using general-purpose programming languages such as Java can be greatly beneficial.

After completing this module, the students can enhance further their skills by studying the methods for automated deduction, semantic disambiguation and language translation.

All three artificial intelligence fundamental modules will be assessed by combining coursework (60%) and exams (40%). Coursework will assess your skills in using AI methods and tools, while exams will evaluate your level of understanding and decision-making skills in analysing and designing intelligent systems.

The specialist modules will be assessed by two pieces of coursework, to assess your knowledge of technologies (50%) and practical ability to use tools (50%). The skills you’ll develop through these assessments will allow you to add intelligence to solutions in your chosen domain – business, industry or entertainment.

The dissertation project will assess your theoretical knowledge, analytical and technological skills, as well as your decision-making potential.