Applied Artificial Intelligence MSc

This module aims to equip you with practical knowledge in statistics required for assessment and quantification of uncertainties in real life scenarios of data analysis. Module presents practically important algorithms of statistical learning for both prediction and decision making purposes and provides the opportunities for their experimental evaluation during the lab sessions. Tools for evaluation of learning algorithms’ performance are also considered and implemented to practical examples.

• Introduction to statistical learning
• Statistics fundamentals: probability, random variables, description statistics and stochastic processes
• Statistical inference: estimation and testing, evaluation metrics
• Bayesian methods: Naive Bayes and Bayesian Networks
• Markov processes and chains, Kalman estimators
• Statistical modelling and decision making: regression, mixture models and classification approaches
• Case study: application of statistical learning for aerospace sector problem.

This module aims to enable you to apply the principles of Systems Engineering to system problems.

Topics covered by the course include:

• Systems challenges
• The systems process
• Understanding systems
• Capability need and requirements
• System design and architecture
• System evaluation, verification and validation
• The impact of organisation on Systems Engineering
• People, skills and competencies in Systems Engineering.

The aim of this module is to enable you to think critically about technology, solutions, and gain best practices of intelligent systems issues relating to the cyber-physical systems.

• Cyber-physical systems: Control, sensor and actuators
• Intelligent agent and multi-agent 
• Intelligent robotics  
• Embedded systems 
• Connected system 
• Countermeasures. 

On successful completion of this module you should be able to: 

• Appraise the theoretical and practical aspects for intelligent system in cyber-physical systems approach.
• Distinguish the fundamental aspect of intelligent agent, robotics, multi agent systems. 
• Create working knowledge in dependable control, and embedded systems. 
• Assess key issues of connected system within the physical world.
• Analyse different approaches of cyber-physical system with consideration of countermeasures.

The module aims at giving you a solid background introduction to optimization and decision theory. The use of modern optimisation methods, especially for applications in artificial intelligence. More than the traditional linear techniques, non-linear techniques will also be addressed, including multi-criteria methods. 

• Introduction to Optimisation and Decision Theory  
• Optimisation models and methods  
• Integer and Mixed-integer programming 
• Linear and non-linear programming (including intro to Meta-heuristics) 
• Decision analysis  
• Multiple-criteria decision analysis.

On successful completion of this module you should be able to:

1. Formulate decision problems based on optimisation scenarios, identifying the different variables. 
2. Differentiate and apply different optimisation models and methods. 
3. Evaluate optimisation and decision problem criteria. 
4. Design decision analysis problems and apply/implement different algorithms and solutions.
5. Examine and debate the application of structured approaches to support decision in context of multiple conflicting objectives.

This module aims at providing the foundations of logic and reasoning modelling to understand how reasoning can be automated. Moreover it introduces the fundamental techniques and formal language to design automated reasoners.

• Introduction to logical representation and reasoning
• Logical Agents
• Propositional Logic
• First-order Logic
• Inference Algorithms
• Engineering domain knowledge representation
• Exercises and case studies

On successful completion of this module a student should be able to:

1.    Analyse syntax and semantics of different problem instances using the appropriate formal language to identify relevant knowledge

2.    Building essential but complete Knowledge Bases representing a given knowledge, using Propositional and First Order Logics as representation languages.

3.    Design strategies to enable automated solving of assigned reasoning problems, detailing the execution steps to perform the given tasks.

This module will introduce you to data analytics, overview challenges and solutions in this area, present approaches to predictive and descriptive data mining and explain unsupervised learning techniques suitable for new information discovery. Visualisation tools and performance metrics are also considered within the module. You may benefit from knowledge of basic concepts of statistics for performance assessment and evaluation. 

• Introduction to Data Analytics
• Data exploration and pre-processing
• Predictive analytics: regression and classification methods
• Clustering and dimensionality reduction
• Graph analysis and visualisation
• Software and tools for data analytics
• Case study: application of data analytics techniques and visualisation tools for knowledge discovery problem.

On successful completion of this module you should be able to:
1. Distinguish stages of the data analytics workflow
2. Categorize data analysis and visualisation techniques with respect to data analytics stages 
3. Plan data analytics workflow based on the available data and formulated requirements
4. Set up algorithms for discovery of new information from the large data sets
5. Evaluate performance of the algorithms and quality of the data analysis outcomes.

The aim of this module is to introduce you to machine learning algorithms, with particular emphasis on supervised learning and deep learning, suitable for real life problems concerning relevant industrial applications from aerospace, manufacturing or transports.

• Artificial Neural Networks (Shallow models)
• Backpropagation and Training
• Deep learning architectures
• Convolutional Neural Networks
• Recurrent neural networks
• Deep learning applications: object detection, identification, classification, tracking, prediction
• Introduction to Reinforcement learning
• Tensorflow practical sessions on Artificial, Convolutional and Recurrent Neural Networks. 

On successful completion of this module you should be able to:

1.    Explain fundamental meaning and discuss applicability of machine learning algorithms for industrial applications.

2.    Test the commonly used AI algorithms and describe their applications.

3.    Implement AI algorithms, estimate their performance in a simulation environment and assess their performance for a realistic case study.

4.    Judge AI implementation platforms and create deep learning applications for specific problems.

Technology typically advances much faster than society develops an understanding of its potential consequences, and before appropriate regulations and standards are developed to prevent negative impacts. This module aims to provide you with a range of knowledge from different ethical, regulatory and social perspectives that will prepare them for the advancing landscape of AI. It will address fundamental theoretical and practical ethical issues (e.g. data management and security, personal control and consent, biases, etc.) and explore them in relation to current standards and regulatory frameworks. In addition, the module will cover a range of key social science topics and techniques to equip you with an appreciation of how to optimise AI integration and the ability to apply practical methods to do so. 

The sessions in this module will focus on three inter-related strands: ethical, regulatory, and social aspects of AI.

• Ethical aspects: exploring and debating ethical principles for AI, from fundamental definitions and the development of moral norms and expectations in society to more specific appraisal of how ethics will shape, and be shaped by, the developing landscape of intelligent systems. Key concepts will be appraised and related to context via classroom activities and discussions to predict possible and probable challenges in dealing with ethical behaviours in the design and application of AI and the potential human / social impacts.
• Regulatory aspects: examining and evaluating the current state of regulations for AI, how formal standards and guidance specifications are developed, and how they are harmonised and linked to legal frameworks. Existing standards / regulatory documents will be reviewed (subject to availability and access permissions) to facilitate group and individual appraisals of content and usefulness in practice, and to predict potential gaps with respect to the evolving landscape of AI in society today and in the future. 
• Social aspects: identifying and assessing the various current and future social implications of AI, with particular focus on the bi-directional impacts between systems and the surrounding cultural and social environment. Psychological processes and human factors that influence attitudes and behaviours towards intelligent systems and their applications in society will be studied, along with practical human / social data collection, measurement and analysis techniques.