Start Date between Sept 23 - July 26

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.

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 module will provide an introduction to linguistic theory and terminology. Students will develop understanding of and the ability to use algorithms and resources for implementing and evaluating text mining and analytics systems. Students will be supported to develop solutions using open-source and commercial toolkits, and will be encouraged to consider the applications of data mining and text analytics through case studies in information retrieval and extraction.

This module is designed to teach fundamental concepts of Reinforcement Learning and intelligent agents, focusing on robotic planning, and control.

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.

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

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 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 module will provide an introduction to linguistic theory and terminology. Students will develop understanding of and the ability to use algorithms and resources for implementing and evaluating text mining and analytics systems. Students will be supported to develop solutions using open-source and commercial toolkits, and will be encouraged to consider the applications of data mining and text analytics through case studies in information retrieval and extraction.

This module is designed to teach fundamental concepts of Reinforcement Learning and intelligent agents, focusing on robotic planning, and control.

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.