Applied Artificial Intelligence MSc

Cranfield University

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.

The aim of this module is to provide you with some of the most used deep learning techniques applied in the decision-making process of autonomous systems. The module presents practically important algorithms used for real-world human-machine, machine-environment, and human-machine-environment applications in transportation, manufacturing and aerospace. The module presents some case studies for experimental evaluation during the lab sessions.

The aim of this module is to introduce you to advanced deep learning algorithms, architectures, and development platforms, with particular emphasis on the computer vision domain for perception, reasoning, and prediction of real-world objects, suitable for real-life problems concerning relevant industrial applications from aerospace, manufacturing or transport.

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.

Multi-agents have the capability to execute many complex and risky tasks when they learn to cooperative better. Multi-agent behavior is ubiquitous around us such as flocking of birds, collective opinion of a society and so on. Agents usually form a network by interacting with each other (connectivity) and different kinds of interaction lead to different multi-agent behaviors. Hence, it is imperative for you to study how agents interact to evolve, how fast they achieve a common agreement and how good is their network in absorbing a sudden shock. Robotics being a huge application of this theory emphasises the need to study how agents plan together to move from a source to destination.

The module aims to give you a solid background introduction to optimisation 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.

The aim of this module is to empower you to critically analyse cyber-physical system enabling technologies, and to synthesise best practices for their design and evaluation. Applied development frameworks and performance metrics are also explored.

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 your experimental evaluation during the lab sessions. Tools for evaluation of learning algorithms’ performance are also considered and implemented to practical examples.