COURSE CURRICULUM

The Artificial Intelligence program is designed to provide students with a comprehensive understanding of AI techniques and their practical applications in engineering, business, and robotics. In the first block, Neural Systems and Natural Language Processing, students study the history, paradigms, and applications of neural networks, along with a deep learning-based approach to natural language processing using Python and tools such as Jupyter and PyTorch. This equips students with essential skills for solving complex AI problems across multiple domains. Block 2, Artificial Intelligence for Mobile Robots, introduces students to mobile robot systems, including hardware and software architectures, sensor systems, low-level programming, and autonomous navigation. Students learn to construct odometry systems, maps, navigation plans, and localisation techniques, with advanced discussion on simultaneous localisation and mapping. In Block 3, Fuzzy Logic and Evolutionary Computing, students explore computational intelligence techniques, beginning with fuzzy logic paradigms and their applications to real-world problem domains. The module then covers evolutionary computing, focusing on heuristic optimisation methods inspired by evolution and collective intelligence, applicable in engineering, economics, and applied sciences. Block 4, Research Methods and Applications, provides a foundation in quantitative and qualitative research methodologies, including laboratory evaluations, surveys, case studies, and action research. Students learn to design research, conduct literature reviews, collect and analyse data, and manage projects, while addressing funding and ethical considerations. Blocks 5 and 6, the Thesis Project, allow students to formulate research questions, write proposals, and manage their dissertation projects, integrating skills gained throughout the program. Discussion boards encourage reflection, critical appraisal, and communication within the cohort, fostering transferable skills valuable for academic progression or graduate employment. Collectively, these modules ensure students develop theoretical knowledge, practical expertise, and research competence, preparing them to contribute effectively in AI-driven industries such as robotics, software engineering, control systems, and data-driven business solutions.