This module is a comprehensive, hands-on program designed for second-year students specializing in AI.  The curriculum moves beyond foundational concepts to focus on practical application and professional skill development.  It is structured into six distinct modules, each worth 5 credits, covering advanced topics from sophisticated machine learning and deep learning to the critical aspects of AI ethics, governance and real-world implementation.  The course’s primary aim is to equip students with the ability to not only build and optimize complex AI models but also to understand how to integrate these solutions into enterprise environments and professional workflows. The teaching and learning strategies emphasize a highly practical, project-based approach.  Students will spend a significant amount of time in guided labs and hands-on notebooks, working with real-world datasets and modern AI frameworks like TensorFlow and PyTorch.  The course structure encourages a transition from theoretical knowledge to applied skills through various project simulations, case studies and peer-reviewed exercises.  Key assessments, such as lab notebooks, project reports and a workflow case study, are designed to test practical implementation skills and the ability to critically analyze and document their work. The capstone of the course is a dedicated mini-project and portfolio integration sprint.  These final modules serve to consolidate all the skills learned throughout the year.  Students will design and build their own domain-specific AI solution, culminating in a working model and a final report.  The Portfolio Integration Sprint then guides them to professionally document their projects, prepare presentations and reflect on their skill development.  This ensures that by the end of the course, students not only have a strong technical foundation but also a tangible, professional portfolio that demonstrates their capabilities to potential employers, aligning with your interest in future AI & Expert Systems.