Course Descriptions

This course aims to establish a comprehensive foundation in human-centred design principles and their practical application in artificial intelligence development. It equips students with the essential knowledge and skills to implement design thinking methodologies throughout the entire AI product lifecycle, from initial concept to final deployment. Students will develop the ability to explain fundamental design principles, demonstrate understanding of AI product development cycles, and apply systematic approaches to identify user needs and define solution requirements. The course further enables students to recognize the critical importance of user experience and interface design in AI systems, analyse existing AI products using established design frameworks, and understand the role of prototyping and iterative testing. Additionally, students will learn to anticipate emerging trends in AI design, gaining both theoretical knowledge and practical insights through real-world case studies of successful AI implementations across various industry sectors.

This course provides a balanced blend of control systems theory and practical data analytics in industrial settings. Students will learn about modelling, analysis, and design techniques for dynamic systems, gaining a solid foundation in both classical and modern control strategies. The curriculum emphasises the importance of feedback mechanisms and stability analysis, enabling participants to effectively design and implement control systems tailored to various applications. In addition to control theory, the course delves into industrial analytics, highlighting the significance of system identification and data-driven decision-making in optimising industrial process control. Real-world case studies will illustrate how analytics can be applied to enhance operational efficiency, reduce downtime, and drive innovation.

This course aims to equip students with comprehensive skills in using AI to design, optimize, and scale AI products for diverse applications, enabling them to effectively leverage generative AI models to solve real-world business challenges. Through hands-on exercises and case studies, students will master advanced techniques for creating sophisticated AI architectures, refining model interactions, and deploying scalable AI solutions across organizational workflows, while addressing critical operational aspects including output consistency, bias mitigation, cost efficiency, and ethical considerations. By integrating technical AI engineering expertise with strategic business objectives, the course prepares students to develop innovative, scalable, and ethically sound AI-powered software solutions that meet evolving market demands.

This course aims to equip students with the technical mastery and creative mindset to harness generative artificial intelligence as a transformative tool for innovative design. Moving beyond the analytical focus of traditional deep learning, it explores advanced architectures—including Generative Adversarial Networks (GANs), diffusion models, and transformer-based systems—for the synthesis of original visual, multimodal, and conceptual outputs. Students will learn to train, fine-tune, and ethically direct generative models to produce and iterate design prototypes, effectively bridging computational techniques with human-centred creative processes. Through hands-on projects, the course cultivates the ability to integrate generative AI into the design workflow, empowering future innovators to leverage cutting-edge technology in the creation of novel products, experiences, and solutions.

This course aims to explore the integration of Artificial Intelligence with Internet of Things technologies to create intelligent, connected systems. Students will examine how AI algorithms enhance IoT systems through real-time data analysis, predictive maintenance, and autonomous decision-making across various industry applications. The curriculum covers the architecture of AI-driven IoT ecosystems, including sensor networks, edge computing platforms, and cloud-based analytics systems, with particular focus on scalability, security, and interoperability requirements. Through case studies and hands-on projects, participants will learn to design and implement AIoT solutions for applications such as smart environments, predictive maintenance, and autonomous systems. The course further addresses the data lifecycle management from collection to actionable insights, and the organizational challenges of deploying and maintaining connected intelligent systems.

This course aims to provide comprehensive knowledge and practical skills for integrating AI components into cohesive, large-scale operational systems across both software and hardware environments. Students will learn to architect and manage the end-to-end lifecycle of AI systems, addressing unique challenges such as data pipeline orchestration, model deployment, API management, and continuous integration/continuous deployment (CI/CD) for machine learning operations (MLOps). The curriculum covers key aspects including system scoping, interoperability with existing enterprise IT infrastructure, performance monitoring, and maintaining system reliability post-deployment. Through real-world case studies and practical exercises, participants will develop competencies in designing scalable AI system architectures, implementing integration protocols, and ensuring seamless communication between AI models and business applications. The course further enables students to manage version control for models and data, oversee system health, and ensure robust security practices throughout the AI system lifecycle.

This course aims to provide students with a comprehensive framework for developing and launching AI-driven ventures, serving as the culminating experience that integrates knowledge from across the curriculum. Students will learn to identify market opportunities for AI solutions, develop viable business models, and create comprehensive go-to-market strategies for AI products and services. The curriculum guides participants through the entire entrepreneurial process, from initial concept validation and prototyping to funding strategies and scaling operations. Through hands-on project development, students will create detailed business plans, build minimum viable products, and pitch their AI ventures to potential investors and stakeholders. The course emphasizes practical skills in market analysis, financial planning, intellectual property protection, and regulatory compliance specific to AI technologies. Students will also develop competencies in building effective teams, securing resources, and navigating the startup ecosystem while addressing the unique challenges of AI entrepreneurship, including ethical considerations, technical feasibility, and market adoption barriers.

This course aims to explore the principles and practices of designing effective interactions between humans and artificial intelligence systems. Students will examine various models of human-AI collaboration, from AI-assisted decision making to fully cooperative problem-solving environments. The curriculum covers fundamental concepts of interaction design, including transparency, trust calibration, and control allocation in AI systems. Through case studies and practical exercises, participants will learn to design interfaces that communicate AI capabilities and limitations clearly, while developing strategies for maintaining human agency and oversight. The course further addresses the psychological and social aspects of human-AI interaction, including user acceptance, cognitive load management, and the ethical implications of increasingly autonomous systems. Students will gain hands-on experience in prototyping and evaluating human-AI interfaces across different application domains, preparing them to create collaborative systems that enhance rather than replace human capabilities.

This course aims to provide students with comprehensive knowledge and practical skills in developing AI-powered products—encompassing both software and hardware implementations— through systematic prototyping methodologies. The course covers the entire product development lifecycle, from ideation and concept validation to functional prototyping and user testing. Students will learn to apply agile development principles, create minimum viable products (MVPs), and utilize various prototyping tools and platforms specific to AI applications. The curriculum emphasizes hands-on experience in transforming conceptual AI solutions into tangible prototypes, incorporating user feedback loops, and iterating designs based on testing outcomes. Participants will develop competency in selecting appropriate prototyping techniques for different AI product categories, assessing technical feasibility, and evaluating market viability. Through case studies and practical projects, students will gain essential skills in documenting development processes, presenting prototypes to stakeholders, and preparing AI products for pilot deployment and scaling.

This course aims to explore the integration of artificial intelligence technologies with traditional business analytics to drive data-informed decision-making and strategic insights. Students will learn how machine learning algorithms, natural language processing, and predictive modelling can enhance conventional analytical approaches across various business functions. The curriculum covers the application of AI techniques to improve data processing, pattern recognition, and forecasting accuracy in areas such as customer behaviour analysis, market trend prediction, and operational optimization. Through hands-on projects using industry-standard tools, participants will develop skills in implementing AI-driven analytical solutions, interpreting complex model outputs, and translating technical findings into actionable business recommendations. The course further addresses the challenges of data quality management, model validation, and ethical considerations in AI-enhanced analytics, while emphasizing the importance of maintaining human oversight in the analytical process.

This course aims to examine the transformative impact of artificial intelligence on modern marketing practices and strategic planning. Students will learn how AI technologies are reshaping customer segmentation, personalization, campaign optimization, and marketing return on investment. The curriculum covers the implementation of machine learning algorithms for predictive analytics, natural language processing for consumer insights, and computer vision for visual content optimization. Through real-world case studies and practical exercises, participants will develop skills in leveraging AI tools for automated customer journey mapping, dynamic pricing strategies, and intelligent content creation. The course further addresses the integration of AI-powered marketing platforms with existing business systems, while emphasizing ethical considerations in data usage, consumer privacy protection, and maintaining brand authenticity in automated marketing environments. Students will learn to develop comprehensive AI-driven marketing strategies that enhance customer engagement, improve conversion rates, and deliver measurable business outcomes.

This course aims to provide students with the principles and practices of creating effective and engaging digital interfaces, with a focus on user-centred design methodologies for AI-enhanced applications. o prior design experience is required; the curriculum starts from foundational visual design principles, including layout, typography, colour theory, and iconography, before advancing to interaction design patterns specific to data-rich and AI-driven interfaces. Students will learn to apply design thinking processes to develop intuitive and accessible digital products that effectively communicate complex information and functionality. The curriculum covers visual design fundamentals, including layout, typography, colour theory, and iconography, as well as interaction design patterns specific to data-rich and AI-driven interfaces. Through practical projects, participants will gain hands-on experience in creating wireframes, mock-ups, and interactive prototypes using industry-standard design tools, while learning to conduct usability testing and incorporate user feedback into iterative design improvements. The course also addresses designing for multiple platforms and devices, ensuring consistent user experiences across different digital touchpoints, and effectively collaborating with development teams to bridge design concepts and technical implementation.

This course aims to explore the application of artificial intelligence and advanced technologies in transforming traditional manufacturing and operations management. Students will examine how AI-driven systems enhance production processes, supply chain coordination, and operational efficiency in smart manufacturing environments. The curriculum covers the integration of IoT sensors, robotics, and machine learning algorithms for predictive maintenance, quality control, and resource optimization. Through case studies and practical simulations, participants will learn to design and implement digital twin technologies, automated production systems, and intelligent inventory management solutions. The course further addresses the strategic implementation of Industry 4.0 technologies, including cyber-physical systems and cloud manufacturing platforms, while focusing on data-driven decision making for operational excellence. Students will develop competencies in optimizing end-to-end production workflows, reducing operational costs, and improving product quality through AI-powered manufacturing systems.

This course aims to provide students with comprehensive knowledge and practical skills in leveraging GPU architecture and parallel programming techniques for accelerating artificial intelligence applications. Students will explore the fundamental principles of parallel computing and learn how to harness the massive computational power of modern graphics processing units for training and deploying complex AI models. The curriculum covers GPU architecture fundamentals, parallel algorithm design patterns, and optimization strategies specifically tailored for machine learning workloads. Through hands-on programming exercises using industry-standard frameworks like CUDA and OpenCL, participants will develop practical skills in implementing parallel versions of common AI algorithms, including neural network training, computer vision processing, and natural language understanding tasks. The course further addresses performance analysis and debugging techniques for GPU-accelerated applications, memory optimization strategies, and best practices for scaling AI workloads across multiple GPU systems. Students will gain essential competencies in maximizing computational efficiency and reducing training times for deep learning models, preparing them to tackle large-scale AI challenges in research and industrial applications.

This course aims to provide a comprehensive understanding of the ethical, legal, and safety considerations essential for developing and deploying artificial intelligence systems responsibly. Students will examine the evolving regulatory landscape governing AI technologies across different jurisdictions and industry sectors. The curriculum covers fundamental ethical frameworks, including fairness, accountability, transparency, and privacy protection, with practical applications in AI system design and implementation. Through case studies and scenario-based learning, participants will learn to identify and mitigate potential biases in AI models, assess algorithmic impacts on diverse stakeholders, and implement safety measures throughout the AI lifecycle. The course further addresses emerging standards and best practices for AI governance, risk management, and compliance, while exploring methods for ensuring algorithmic transparency and explainability. Students will develop the skills necessary to create AI systems that not only comply with legal requirements but also align with societal values and promote trust among users and stakeholders.