This module is targeted to Master students who do not have STEM (Science, Technology, Engineering, and Mathematics) background. The module will provide a general introduction to the subject matter relating to artificial intelligence and machine learning. It will cover fundamental ideas in problem formulation and solving, knowledge representation and machine learning techniques. It will provide students with the appropriate background that is necessary to undertake further investigation into advanced and specialized artificial intelligence and machine learning modules. Students will learn key theoretical concepts, state-of-the-art techniques and research advances in the field of intelligent systems. Upon completing the module, students will have a holistic understanding of how artificial intelligence works in principle and in applications. In particular, students will have a high-level understanding of artificial intelligence and machine learning techniques coupled with hands-on introductory programming. The module will use case studies to support the tutorials that are developed from research and industrial projects. There will be opportunities throughout the delivery of the module to attend tutorial where students can discuss and practice designing solutions and gain feedback from module tutors. This module will be delivered using lectures and workshops. Lectures will impart to students an inclusive high-level understanding of artificial intelligence and its technologies, to help students to develop a comprehensive understanding of the technological background to the subject matter. The students will learn through a combination of learning and teaching methods and directed independent learning. The students will learn about concepts, mathematical principles and theories in formal lectures and these will be demonstrated, practiced and discussed in tutorial sessions. Lectures will offer the students the opportunity to develop knowledge of the principles and concepts of different approaches in artificial intelligence. The tutorials will be used to reinforce learning from the lectures through the use of examples and case studies which the students will research, discuss and receive feedback from the tutors. Tutorials will enable the students to develop ability to think independently and make judgements on matters relating to artificial intelligence and other machine learning techniques. During tutorial sessions, the students will analyse and solve machine learning problems. A guided independent learning homework exercise will also be set for the students to work on outside of class time. These will include problems in artificial intelligence intended to aid understanding. The rationale for the problem solving tasks during workshops is to support learning by addressing or finding solutions for real-world scenarios. The aim is to broaden and deepen your understanding of the theoretical and practical aspects of the subject, which in turn will help broaden professional skills and abilities.