Human intelligence involves a continuous process of sensing, analysing, reasoning, and adaptation. In this course, we will focus on studying algorithms (both learning and optimization), which could be used to develop an artificial intelligence (AI) system. Given the limited time, we will try to eliminate mathematical proofs, as much as possible, and focus more on the theory and implementation using different programming languages.

This module looks at different data processing and mining techniques and state of the art data processing and mining tools. The topics include introduction to data mining and knowledge discovery process, data collection and processing with various data types from variety of sources, social web mining. The module will look at a variety of techniques and trends in data mining.

This module's main emphasis is on the fundamentals of deep machine learning (ML), covering both basic and advanced concepts, including philosophy, linear algebra, big data, optimization, and information theory.

The material in this module is carefully designed to meet students’ needs and requirements for the programme of study alongside essential project management skills dictating research activities. Students will be exposed to a wide variety of tools, techniques, methodologies and processes in the field of project management. Ethical and legal aspects of project planning and management will also be discussed.

This module introduces students to research, and methodologies used to underpin scientific work, data analysis, hypothesis’ establishment and artefact validation in understanding research on an appropriate subject discipline. The material in this module is carefully designed to meet students’ needs and requirements for the programme of study. This module covers traditional approaches in literature review as essential preparation for the project stage and draws expertise from other departments within the University.

This is an introductory course in statistics for data science and as such it assumes little prior knowledge of the subject. This module introduces the basics of descriptive statistics (including graphical methods), estimation, linear regression models and inferential statistics. Appropriate software tools will be used to will be used to extract insights from data.

Intelligent agents are programs that can perceive their environment through sensors and can change the state of their environment through actuators. Environments can be fully software based such as a computer game in which an agent can learn how to play or a physical real-world environment where the agent controls a robot and helps it to navigate around obstacles to reach its destination. One technique that sets the training of an agent apart from others is in the way data are presented to the agent. Traditional techniques (such as artificial neural networks or decision tree induction) normally involve presenting data, referred to as a training set, to the training algorithm. A training set consists of records, each one having a number of inputs and an output. The aim of training is for the algorithm to output the correct value to associated with its inputs. Training involves repeatedly presenting the training set to the training algorithm until it gets no better at mapping inputs to output.

This module is designed as an introduction to some of the central concepts and tools used in Data Science. As such it includes discussions on data capture, maintenance, processing, analysis and communication. A series of real-world case studies will be discussed, and state of the art software and algorithms will be used as tools within these case studies.

Whether you decide to progress to further study or to a career in industry, one of the most important skills expected of a Data Scientist is the ability to work creatively under the appropriate guidance of an organisation. It is therefore essential that you use and develop the skills and knowledge gained from other modules and from your wider educational and/or working background in a major integrative exercise. You will be expected to develop an idea (guided by a member of academic staff) and demonstrate your ability to develop it further, producing a suitable artefact by applying your technical, analytical, practical and managerial skills in an integrated manner.