MSc Artificial Intelligence

The module is designed to impart essential mathematical principles and concepts of computer vision alongside its practical applications. The module encompasses core topics in image formation and low-level image processing; mid-level scene representation; model-based description and tracking. Appropriate hardware/software tools will be integrated into the module to enable students to apply and test computer vision algorithms including deep learning on real world data sets.

The objectives of this module are to:

• Enable students to gain understanding of essential concepts of image processing / computer vision algorithms with their practical applicability in real systems.
• Develop students’ expertise in analysing, designing, building, training, and evaluating computer vision deep learning models.
• Train students in using appropriate hardware / software tools for solving common computer vision problems.
• Prepare students with postgraduate level research and report writing skills.

Artificial Intelligence research and development spans over a period of nearly 70 years. During this period the academicians have been trying to address most of the aspects of human intelligence known from other fields – mathematics, philosophy, psychology, linguistics, biology, etc.

As a result, AI was clearly partitioned into several areas, each with its own methodology of investigation and technology of problem solving – state-space problem solving, decision making, automated reasoning, knowledge-based planning and machine learning. While during different periods one or another were attracting the attention, all of them found practical applications and gradually evolved, reaching bigger depth and maturity.

This module covers the evolution of AI and provides a thematic coverage of the various contemporary branches of the discipline. AI paradigms, models, and technologies are covered and students are given the opportunity to investigate and apply those that interest them most.

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 inference. It discusses examples of intelligent systems and studies how to develop intelligent applications such as expert systems, natural language 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.

This module provides a comprehensive overview on the use of data and algorithms to imitate how human learn as a branch of Artificial Intelligence (AI). It also provides practical skills using a programming language such as python for working with various tools to build machine learning solutions.

The knowledge and skills obtained can be used in many tasks where extracting knowledge and gaining insight from data is of crucial importance for the competitiveness and the effectiveness of the businesses – customer profiling, product recommendations, market trends analysis, cybersecurity, investment monitoring, stock price prediction, etc. Some basic programming skills using languages such as Python or other relevant languages is required.

The module aims to strengthen your skills in data technologies ranging from database and data warehousing to Big Data. First, it will provide you 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, you 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, you 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 such as, SQL Server Management Studio (SSMS) and Azure Data Studio, 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 you 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 using tools such as Hive, Pig and Zeppelin for hands-on experience.

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.

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)

Prior knowledge: Understanding of research management, planning and LSEP issues

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.