The Artificial Intelligence MSc is open to all learners who meet the following prerequisites:

• A BSc or BEng Honours degree (2:2 or above) in computing, engineering, or related subject, or an equivalent professional qualification

• Familiarity with discrete mathematics and predicate calculus

• Additional qualification(s) at an Honours level and/or experience(s) in computer science, mathematics, and/or engineering

Additionally, learners are expected to have an IELTS proficiency score of at least 6.0 (no less than 5.5, or equivalent) if English is not their first language.

Certified by the British Computer Society (BCS) and carefully crafted by the experts at the University of Huddersfield, this fully-online degree allows you the freedom and flexibility to fit your studies around your existing commitments, helping you grow your career in a field that harnesses the latest advancements in AI and technology.

With a focus on industry relevance and cutting-edge AI techniques, you'll gain the knowledge and skills needed to excel in a variety of roles, such as a robotics programmer, machine learning engineer, data mining analyst, or software engineer.

You’ll also join a community of like-minded learners helping to shape the future.

Throughout this degree, you’ll delve further into the following concepts:

• Autonomous and Autonomic Intelligent Systems: Explore intelligent systems, addressing technical challenges alongside ethical, legal, and human implications.

• Robotics: Develop specialist knowledge and skills in robotics, learning more about the integration of mechanical devices, sensors, and intelligent agents, as well as their design, development, and programming.

• Artificial intelligence planning: Explore the history and contemporary practices of automated planning, including algorithms, heuristics, and current application areas, like hybrid planning.

• Data mining: Learn data mining techniques to extract valuable insights from large datasets, including preparation, exploration, supervised and unsupervised approaches, and social impact considerations.

• Knowledge Representation and Reasoning (KRR): Gain expertise in representing and utilising information for complex tasks through knowledge representation principles, languages, and algorithms, and learn more about its applications, such as the semantic web and knowledge graphs.

• Machine learning: Master machine learning techniques for various applications, including deep learning, data-driven approaches, and model-driven learning, with practical evaluation and exploration of applications, like AlphaGo.

• Effective research practice: Gain essential skills for a successful computing researcher or practitioner by learning more about research methodologies, how to write research papers, reports and proposals, and effective project management and presentation essentials.