MSc Artificial Intelligence

The aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

The module provides a grounding in the techniques of Knowledge Representation and Reasoning and how they are used in the wider field of Artificial Intelligence. General concepts of this approach are explained, and a range of specific logical representations are introduced for representing different types of information (e.g. temporal and spatial information). Students will learn how to use these representations to encode a variety of real-world problems and how logical inference can be used to solve them. They will also learn how to use software tools to carry out automated reasoning.

The workplace (and home) is (are) going through a technological revolution. New automated algorithms can make medical assessments or deliver financial advice, for example, faster and more accurately than human professionals. Customers are given financial advice by chatbots and other AI systems. Medical data is collected by fitbits and AI can deliver medical judgements, meaning that they have no need to interact with another human at all. And more information about customers and potential customers is collected, collated and analysed every day, allowing for targeted marketing and accurate risk analysis. Ostensibly, these technologies are being introduced in order to make commerce, business and medicine more efficient, more accurate, and more objective. Nonetheless, with new technologies come new problems, and the rapidity with which these technologies are being applied often mean these problems go unnoticed until serious ethical and legal issues emerge. This module provides students with the analytical and theoretical tools to engage with these issues in a professional context. The module is taught online alongside modules in maths and computing in which these issues are live. There are eight online units. The online units will comprise of online learning materials such as videos and online documents, some related reading, and a facilitated online discussion forum per topic. There will be group work and group presentations which focus on discussion of concrete case studies in AI.

Module summary

This module is designed to help students develop and demonstrate the skills required to carry out a substantial individual project in a chosen aspect of Artificial Intelligence. Core skills for conducting the individual project are developed through guided online learning.

Objectives

On completion of this module, students should be able to:

- demonstrate an in-depth comprehension of the topics/issues relevant to the AI project;
- select and apply the appropriate AI methods for their chosen AI project area;
- demonstrate independent application of the knowledge, methods and techniques that have been acquired during the AI MSc programme in producing a solution in the project;
- Normally some elements of software implementation are expected;
- demonstrate skills in project management and presentation;
- produce a critical evaluation on the outcome of the project;
- demonstrate proficiency in written communications;
- understand how to make use of a wide range of information sources.

The aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

Algorithms and algorithmic problem solving are at the heart of computer science. This module introduces students to the design and analysis of efficient algorithms and data structures. Students learn how to quantify the efficiency of an algorithm and what algorithmic solutions are efficient. Techniques for designing efficient algorithms are taught, including efficient data structures, standard methods such as Divide-and-Conquer and Dynamic Programming as well as more advanced techniques for computationally intractable problems and large data sets. This is done using illustrative and fundamental problems relevant to AI.

The module provides a grounding in the techniques of Knowledge Representation and Reasoning and how they are used in the wider field of Artificial Intelligence. General concepts of this approach are explained, and a range of specific logical representations are introduced for representing different types of information (e.g. temporal and spatial information). Students will learn how to use these representations to encode a variety of real-world problems and how logical inference can be used to solve them. They will also learn how to use software tools to carry out automated reasoning.

The workplace (and home) is (are) going through a technological revolution. New automated algorithms can make medical assessments or deliver financial advice, for example, faster and more accurately than human professionals. Customers are given financial advice by chatbots and other AI systems. Medical data is collected by fitbits and AI can deliver medical judgements, meaning that they have no need to interact with another human at all. And more information about customers and potential customers is collected, collated and analysed every day, allowing for targeted marketing and accurate risk analysis. Ostensibly, these technologies are being introduced in order to make commerce, business and medicine more efficient, more accurate, and more objective. Nonetheless, with new technologies come new problems, and the rapidity with which these technologies are being applied often mean these problems go unnoticed until serious ethical and legal issues emerge. This module provides students with the analytical and theoretical tools to engage with these issues in a professional context. The module is taught online alongside modules in maths and computing in which these issues are live. There are eight online units. The online units will comprise of online learning materials such as videos and online documents, some related reading, and a facilitated online discussion forum per topic. There will be group work and group presentations which focus on discussion of concrete case studies in AI.

Module summary

This module is designed to help students develop and demonstrate the skills required to carry out a substantial individual project in a chosen aspect of Artificial Intelligence. Core skills for conducting the individual project are developed through guided online learning.

Objectives

On completion of this module, students should be able to:

- demonstrate an in-depth comprehension of the topics/issues relevant to the AI project;
- select and apply the appropriate AI methods for their chosen AI project area;
- demonstrate independent application of the knowledge, methods and techniques that have been acquired during the AI MSc programme in producing a solution in the project;
- Normally some elements of software implementation are expected;
- demonstrate skills in project management and presentation;
- produce a critical evaluation on the outcome of the project;
- demonstrate proficiency in written communications;
- understand how to make use of a wide range of information sources.

The aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

Algorithms and algorithmic problem solving are at the heart of computer science. This module introduces students to the design and analysis of efficient algorithms and data structures. Students learn how to quantify the efficiency of an algorithm and what algorithmic solutions are efficient. Techniques for designing efficient algorithms are taught, including efficient data structures, standard methods such as Divide-and-Conquer and Dynamic Programming as well as more advanced techniques for computationally intractable problems and large data sets. This is done using illustrative and fundamental problems relevant to AI.

The module provides a grounding in the techniques of Knowledge Representation and Reasoning and how they are used in the wider field of Artificial Intelligence. General concepts of this approach are explained, and a range of specific logical representations are introduced for representing different types of information (e.g. temporal and spatial information). Students will learn how to use these representations to encode a variety of real-world problems and how logical inference can be used to solve them. They will also learn how to use software tools to carry out automated reasoning.

The workplace (and home) is (are) going through a technological revolution. New automated algorithms can make medical assessments or deliver financial advice, for example, faster and more accurately than human professionals. Customers are given financial advice by chatbots and other AI systems. Medical data is collected by fitbits and AI can deliver medical judgements, meaning that they have no need to interact with another human at all. And more information about customers and potential customers is collected, collated and analysed every day, allowing for targeted marketing and accurate risk analysis. Ostensibly, these technologies are being introduced in order to make commerce, business and medicine more efficient, more accurate, and more objective. Nonetheless, with new technologies come new problems, and the rapidity with which these technologies are being applied often mean these problems go unnoticed until serious ethical and legal issues emerge. This module provides students with the analytical and theoretical tools to engage with these issues in a professional context. The module is taught online alongside modules in maths and computing in which these issues are live. There are eight online units. The online units will comprise of online learning materials such as videos and online documents, some related reading, and a facilitated online discussion forum per topic. There will be group work and group presentations which focus on discussion of concrete case studies in AI.