MSc Artificial Intelligence

Learning Outcomes

AI Vision and Deep Learning

By the end of the Module, students will be able to:

• Understand and apply underpinning mathematics and / or physics governing computer vision algorithms / systems.
• Demonstrate sound understating of the theory and operation of image processing and computer vision algorithms / systems, and a critical awareness of current problems and new insights.
• Use software / hardware and modelling tools to analyse and implement selected aspects of computer vison algorithms / systems.
• Develop postgraduate level skills in literature review, critical evaluation of results and report writing by exploring advanced topics and / or recent related to computer vision algorithms / systems. Build intuition behind structuring computer vision Deep Learning projects and hyperparameters tuning.
• Show awareness of legal, social, ethical, and professional (LSEP) issues particularly important in computer vision algorithms and systems.

Advanced AI Technologies

On completing the module students will be able to:

• Understand the differences between classical and advanced problems, paradigms and methodologies in AI and their challenges from ethical, legal, psychological and social point of view
• Learn advanced methods and algorithms for modelling of intelligent reasoning and behaviour
• Develop some interest and ability to do independent study of more complex models, more sophisticated methods and more complex technologies
• Practice modelling of intelligent applications which utilize advanced AI models and methods
• Acquire practical skills for design and development of AI systems which use advanced AI technologies

Artificial Intelligence

On completing the module students will be able to:

• Understand and critically analyse the essential concepts, principles, methods, techniques and problems of AI.
• Have working knowledge of the methods for state space search, qualitative and quantitative assessment of the progress towards goal state, heuristic information representation, retrieval and application to problem solving.
• Demonstrate the understanding of knowledge engineering and ability to develop a prototype of knowledge-based systems which can use knowledge representation and automated logical inference
• Differentiate between different methods for decision making and action planning applicable to the task for building agents which can learn from their own behaviour
• Develop decision making skills based on theioretical and empirical comparison of the different methods and algorithms for buildingintelligent agents
• Understand the Legal, Ethical & Professional Issues brought by AI and their impact on the society

Machine Learning

By the end of the Module, students will be able to:

• Reveal a deep understanding of and demonstrate familiarity with the different methods for machine learning and assess competently their advantages and limitations.
• Develop competence and confidence to make choice of suitable methods and tools for Machine Learning to achieve best possible performance in various business scenarios to drive organisational success.
• Display familiarity with the various tools and technologies for analysis of real-life and toy datasets using programming languages like Python
• Develop competent skills in data visualisation and development and evaluation of machine learning models using tools such as matplotlib and scikit-learn.
• Appreciate and analyse the legal, ethical, and professional Issues of Machine Learning and estimate the impact of Machine Learning on society

Data Warehousing and Big Data

After successfully completing this module, students will be able to:

• Demonstrate competence in the process of developing, configuring, utilising, and managing of data warehouse applications in a variety of contexts using DBMS tools.
• Comprehensive understanding of the principles of organisation, validation, transformation and analysing large volumes of data on specialized platforms (Big Data) from various data sources – files, databases, server logs, etc.
• Demonstrate comprehensive understanding of the advantage and limitations of Big Data technologies, including predictive analytics and build the confidence to interpret data as insights to drive organisational success.
• Demonstrate competence in SQL.
• Understand, appraise, and participate in the legal, social, ethical and professional framework for developing data-intensive systems working in an agile team environment.

Cloud Computing and the Internet of Things

On successful completion of this module students will be able to:

• Design and critically assess the strengths and weaknesses of different IoT system architectures and components, showing understanding of their key features, including (passive and active) sensors, actuators, physical communications layer, message protocols, programming frameworks, and energy and bandwidth constraints
• Apply extensive hands-on application development skills for building multi-tier cloud-based IoT systems as members of a development team and evaluate the strengths and weaknesses of different types of cloud-based architectures
• Express a critical understanding of current research areas associated with the Internet of Things, Cloud Computing and Autonomous Intelligent Systems (AIS), including the commercial context and any privacy/security issues, legal, social, ethical, and professional issues related to the design, development, and implementation of Cloud Computing and IoT technologies and systems
• Apply broad skill in writing professional reports as vehicles for communicating research ideas
• Demonstrate ability for professional presentation, delivery, and peer assessment of research work

MSc Project

On successfully completing this module, students will be able to:

• Design, plan, monitor and manage a piece of original project work
• Produce a clear set of specifications for the project from its initial stage
• Critically analyse previous relevant work by the effective use of libraries and other information sources
• Synthesize knowledge and skills previously gained and apply these to an in-depth project
• Understand ethical, legal and professional issues and apply them to a project
• Integrate theory and practice by applying a range of tools, skills and techniques
• Communicate effectively findings in a variety of ways
• Write a comprehensive and concise report, justify the project implementation, discuss and explain findings at the viva
• Critically evaluate the project outcomes, including evidence of commercial risks.