MSc Artificial Intelligence & Applications

This module aims to ensure that you're aware of the legal, social, ethical and professional issues commensurate with the practice of Information Systems Engineering.

On completion of the module, you'll be able to:

• appreciate the characteristics of professionalism as it relates to modern data management
• recognise and appreciate the professional aspects of other modules in the course, and how those aspects influence practice
• form a sound basis on which you'll subsequently be able to practise Information Systems Engineering with a due regard for legal, ethical and social issues

The aim of this module is to provide you with the foundations of mathematics that are required to understand modern Artificial Intelligence techniques. The module will focus on three topics: probability, statistics and linear algebra.

On completion of the module you'll be able to:

• understand and apply probability theory as used in modern AI:
• randomness
• probability distributions
• variance and expectation
• expected value
• understand and apply statistical techniques as used in modern AI:
• basic data analysis
• significance tests
• Bayesian inference
• understand and use linear algebra techniques as used in modern AI:
• scalars
• vectors
• matrices
• tensors

This module aims to give you an understanding of the challenges posed by big data, an understanding of the key algorithms and techniques which are embodied in data analytics, and exposure to a number of different big data technologies and techniques.

After completing this module, you'll be able to:

• understand the fundamentals of Python to enable the use of various big data technologies
• understand how classical statistical techniques are applied in modern data analysis
• understand the potential application of data analysis tools for various problems and appreciate their limitations
• be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impact

This module focuses on implementing AI algorithms and building autonomous systems. This involves gaining an understanding of what Artificial Intelligence means in the context of autonomous systems, such as the key algorithms and techniques that enable rational decisions.

On completion of the module you'll be able to:

• program in Python, with the goal being to implement key AI algorithms and build AI systems
• define and understand the problem of Artificial Intelligence as it relates to autonomous systems
• apply search techniques to enable autonomous systems to choose actions that are appropriate to their goals
• apply key techniques to adversarial problems, such as Mini-Max and Monte-Carlo Tree search

The most impactful area of AI has been machine learning using neural networks. When combined with reinforcement learning, these neural networks can also become autonomous agents that can, for example, learn to play games to an extraordinarily high standard. This module will cover these two areas of AI.

After completing this module, you'll be able to:

• define and understand the problem of agents that learn
• understand how Deep Reinforcement Learning uses Deep Neural Networks together with Reinforcement Learning in, for example, the Atari Games work of Google Deepmind

This module provides an overview of the application of AI techniques - including those which mimic natural evolutionary processes (genetic algorithms and genetic programming in particular) - to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.

After completing this module, you'll be able to:

• understand the benefits and opportunities for evolutionary computing in the context of financial applications
• understand the principles of evolutionary computation, in particular genetic programming and genetic algorithms, as well as neural networks (particularly those configurations most suited to time series data)
• understand how the computational approaches covered in the class may be applied to financial problem-solving and understand their limitations
• develop and evaluate practical solutions to finance-based problems

This module equips you with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The module balances a solid theoretical knowledge of the techniques with practical application via Python.

After completing this module, you'll be able to:

• understand the aims and fundamental principles of machine learning
• understand the applicability of the algorithms to different types of data and problems, along with their strengths and limitations
• understand and apply a range of the advanced algorithms and approaches to deep learning and machine learning using artificial neural networks and interpret the outcomes

You'll undertake an individual project under supervision, which should contain an element of original research. The project will be AI-application based (i.e. analysing, specifying, building and evaluating an AI-application or demonstrator, and forming recommendations and conclusions on the relative merits of the technologies involved and the methodologies used). The project will include production of developed code, supporting written documentation, and practical demonstration. The project is assessed through a written dissertation.