This course develops reading, writing, listening, and speaking skills for academic and professional contexts. Key topics include phonetics, word formation, sentence structure, grammar, logical reasoning, writing processes, and public speaking. Students also explore ethical communication, copyright issues, and ICT in language learning. By the end, students will communicate effectively in written and spoken English, apply logical reasoning in presentations, and demonstrate proficiency in academic and professional writing.

This course covers set theory, real and complex numbers, sequences, series, quadratic equations, the binomial theorem, mathematical induction, trigonometric functions, the Argand diagram, and De Moivre’s theorem. By the end, students will solve mathematical problems, apply algebraic and trigonometric concepts, and use logical reasoning in quantitative analysis.

This course introduces classical mechanics, covering units, dimensions, vectors, scalars, kinematics, Newton’s laws, work, energy, momentum, rotational motion, gravitation, conservation laws, circular motion, and satellite motion. By the end, students will analyse motion, apply physical laws, and solve problems involving mechanics and energy systems.

This practical course focuses on measurement of physical quantities, error analysis, graphical data representation, and experiments in mechanics, electricity, heat, and light. By the end, students will conduct experiments, analyse data accurately, and present scientific findings effectively.

Descriptive Statistics

This course introduces fundamental computing concepts and their societal applications. Key topics include computer history, system components, hardware and software, information processing, the internet, and emerging trends. Practical components cover operating systems, productivity tools, and digital applications. By the end, students will understand core computing concepts, utilise basic applications, and apply digital tools effectively in academic and real-world contexts.

This course is designed for undergraduate students across all disciplines, including science, technology, humanities, and social sciences, who want to develop structured problem-solving skills applicable in both academic and real-world contexts. Students will gain practical skills in Computational Thinking, including breaking down complex problems (decomposition), identifying patterns, simplifying information through abstraction, and designing step-by-step solutions using algorithms. By the end of the course, learners will be able to approach challenges logically, think critically, and communicate their reasoning clearly. This course is essential because computational thinking is a core 21st-century skill that underpins fields such as data science, artificial intelligence, business analysis, and even everyday decision-making. Whether students pursue careers in technology or not, the ability to solve problems systematically and efficiently is increasingly valuable in a digital world. Learning will take place through a blend of interactive lectures, hands-on practical sessions, collaborative problem-solving tasks, and real-world case studies. Students will engage in activities such as designing algorithms, analysing patterns in data and text, and applying computational strategies to interdisciplinary scenarios. The course also introduces discussions on the ethical and societal impact of computing, preparing students to think responsibly about technology.

This course introduces students to the landscape of entry-level certifications in Artificial Intelligence, providing a structured pathway into the AI profession. Students will explore key concepts such as AI literacy, certification types (vendor-neutral and vendor-specific), and the skills required to succeed in beginner-level certification exams. The course also examines how AI certifications align with career goals and industry expectations, helping students make informed decisions about their professional development. Through guided activities, learners will engage with certification resources, analyse exam formats, and practise foundational concepts required for certification success. Using a combination of case studies, hands-on preparation tasks, and collaborative discussions, students will begin preparing for at least one entry-level AI certification while developing responsible and ethical perspectives on AI use.

This course introduces effective use of library resources and ICT tools for academic learning. Key topics include library history and types, ICT applications, internet resources, data communication, electronic information systems, web technologies, and ethical issues. By the end, students will access, evaluate, and utilise information resources effectively for academic and professional purposes.

This course explores current health challenges, including non-communicable and infectious diseases, diet and health, women’s health, healthcare ethics, drug use, environmental health, and global health issues. By the end, students will analyse contemporary health challenges and apply preventive strategies to improve population health.

This course examines human-environment relationships, energy resources, environmental pollution, waste management, sustainable development, and environmental challenges in Nigeria. By the end, students will evaluate environmental issues and contribute to sustainable development initiatives.

This course examines Nigerian history, culture, and socio-political development from pre-colonial times to the present. Key topics include major ethnic groups, colonial rule, nationalism, independence, nation-building challenges, indigenous trade, social justice, citizenship, social vices, and national re-orientation programmes. By the end, students will analyse Nigeria’s cultural foundations, evaluate nation-building challenges, and apply civic responsibilities to promote national development.

Problem-Solving

General Mathematics II

This course covers electricity and magnetism, including electrostatics, electric fields, potentials, Coulomb’s and Gauss’s laws, capacitance, DC circuits, magnetic fields, Ampère’s and Biot-Savart laws, electromagnetic induction, Faraday’s and Lenz’s laws, Maxwell’s equations, transformers, and AC circuits. By the end, students will analyse electrical and magnetic systems, solve circuit problems, and apply electromagnetic principles.

This practical course covers measurement of physical quantities, data collection, error analysis, graphical analysis, interpretation of results, and scientific reporting. By the end, students will conduct experiments, analyse and interpret data, and present scientific findings clearly and accurately.

This course introduces students to the principles of Computational Intelligence, with a focus on fuzzy logic and inference systems for handling uncertainty and imprecision in real-world problems. Students will explore key concepts such as fuzzy sets, membership functions, linguistic variables, and approximate reasoning, as well as the design and operation of fuzzy inference systems. The course emphasises the practical application of fuzzy logic in domains such as control systems, decision support, and robotics, highlighting how these approaches differ from traditional rule-based systems. Students will also gain experience in building and simulating fuzzy systems using tools like MATLAB or Python-based libraries. Through a combination of lectures, hands-on labs, case studies, and collaborative exercises, students will design simple fuzzy rule-based models, evaluate their performance, and explore hybrid approaches that combine fuzzy logic with other intelligent techniques.

This course provides students with a foundational understanding of the hardware systems that power modern Artificial Intelligence and robotics applications. Students will explore key components such as CPUs, GPUs, TPUs, and embedded systems, as well as memory, storage, and networking technologies required to support AI workloads. The course highlights how different hardware configurations impact the performance of AI training and inference, enabling students to evaluate and select appropriate systems for specific tasks. It also introduces sensors, actuators, and communication interfaces used in intelligent and robotic systems, bridging the gap between computing hardware and real-world applications. Through practical activities, benchmarking exercises, and system configuration tasks, students will learn to set up and optimise AI-ready environments, both on local machines and cloud platforms. The course also examines emerging trends such as edge AI and energy-efficient computing, preparing students to understand the future direction of AI hardware systems.

This course introduces students to the practical use of modern Artificial Intelligence tools and how they can be combined to create efficient, end-to-end workflows. Students will explore a wide range of AI tools for text, image, audio, and video generation, as well as techniques for improving outputs through effective prompt design. The course focuses on building real-world workflows by integrating multiple AI tools to accomplish tasks such as content creation, research, marketing, and productivity. Students will learn how to design structured AI-assisted processes that enhance efficiency and creativity across different domains. Through hands-on activities, tool demonstrations, and project-based tasks, students will experiment with combining AI tools into cohesive pipelines while also examining ethical, legal, and responsible-use considerations in AI-driven work environments.

Entrepreneurship and Innovation

Mathematical Methods I

Computer Programming I

Discrete Structures

Digital Logic Design

Introduction to Data Science

This course introduces students to the foundational concepts and techniques of Artificial Intelligence, providing a broad understanding of how intelligent systems are designed and applied. Students will explore key ideas such as intelligent agents, search strategies, knowledge representation, and problem-solving methods in AI. The course examines how machines reason, learn, and make decisions, including concepts like heuristics, logical reasoning, and natural language processing. Students will also engage with classic ideas such as the Turing Test and explore how AI systems differ from human intelligence. Through lectures, practical exercises, and problem-solving activities, students will analyse real-world AI applications, represent knowledge using logical structures, and explore programming tools used in AI development. The course also introduces expert systems and emerging applications such as image recognition, preparing students for more advanced study in AI.

This course provides students with supervised industrial training through the Students Industrial Work Experience Scheme, offering practical exposure to real-world computing and Artificial Intelligence environments. Students are attached to organisations where they gain hands-on experience, observe workplace practices, and understand how technology-driven teams operate. The course enables students to develop professional skills, apply classroom knowledge in real settings, and gain insight into industry tools, workflows, and responsibilities. It also prepares students for future careers by building workplace readiness and technical confidence. Through structured supervision, workplace activities, and reflective documentation, students will record their experiences, analyse the skills acquired, and present a comprehensive report of their training, including a formal defence of their learning outcomes.

This course builds on foundational knowledge to prepare students for intermediate-level certifications in Artificial Intelligence and machine learning. Students will explore certification pathways, analyse exam blueprints, and develop the applied skills required for associate-level certification exams. The course focuses on practical competencies, including working with industry-standard tools, understanding data and model evaluation concepts, and completing tasks commonly featured in certification assessments. It also helps students align certification choices with their evolving career goals. Through hands-on exercises, mock exam scenarios, and guided preparation activities, students will strengthen their readiness for certification while engaging with ethical and professional considerations in AI practice.

Philosophy, Logic and Human Existence

Linear Algebra II

Computer Programming II

This course equips students with the skills and practices necessary for effective Collaborative Programming and code maintenance in team-based software projects. Students will explore version control, coding standards, debugging, and collaborative workflows to ensure high-quality, maintainable code. The course emphasizes hands-on experience with tools such as Git, GitHub, GitLab, and IDEs, while teaching students how to manage feature branches, perform code reviews, and resolve merge conflicts. Students will also learn Agile and DevOps practices, refactoring techniques, and strategies for maintaining legacy codebases. Through practical exercises, team projects, and open-source collaboration activities, students will develop proficiency in teamwork, project management, and professional coding ethics, preparing them to contribute effectively in real-world software development environments.

This course introduces students to the fundamentals of Blockchain Technology and its applications in artificial intelligence. Students will explore blockchain architecture, cryptographic techniques, consensus mechanisms, and the development of smart contracts on platforms such as Ethereum and Hyperledger. The course emphasises practical applications of blockchain across sectors including finance, supply chain, healthcare, and AI-driven solutions. Students will gain hands-on experience setting up blockchain environments, deploying smart contracts, and integrating AI into blockchain-based projects. Through lab exercises, projects, and case studies, learners will develop the skills to evaluate blockchain platforms, design secure and efficient systems, address scalability and interoperability challenges, and understand the ethical, legal, and regulatory considerations associated with blockchain adoption.

This course introduces students to Explainable Artificial Intelligence (XAI) and its role in designing AI systems that are transparent, interpretable, and user-friendly. Students will explore human-centred design principles, methods for enhancing AI explainability, and approaches for evaluating user experience in AI-driven applications. The course emphasises the trade-offs between model performance and interpretability, as well as ethical considerations in deploying AI in critical domains. Through case studies, practical design exercises, and analysis of real-world applications in healthcare, finance, and decision-making systems, students will learn to create AI solutions that are both effective and understandable to users.

This course introduces students to Prompt Engineering and the principles of designing effective interactions with AI systems. Students will learn how prompts influence AI behaviour and how to structure them for tasks involving text, images, audio, and multimodal outputs. The course emphasizes techniques for improving prompt accuracy, reasoning, and consistency, as well as iterative testing and refinement. Students will apply prompt engineering to real-world problems while considering ethical, safety, and bias implications. Through practical exercises, case studies, and structured experimentation, learners will develop the skills to design, evaluate, and optimise prompts for a variety of AI applications.

AI and Information Literacy in the 21st Century

Data Structures

This course introduces students to the principles and practice of Machine Learning using Python. Students will explore supervised, unsupervised, and reinforcement learning, understanding how each approach applies to different problem domains and how machine learning relates to AI and deep learning. The course emphasises framing real-world problems as machine learning tasks, implementing statistical learning algorithms, evaluating model performance, and addressing challenges such as overfitting and bias-variance trade-offs. Students will also gain a foundational understanding of neural networks and generative models, including large language models. Through hands-on lab exercises, students will preprocess data, encode features, implement algorithms such as linear regression and decision trees, and optimise models for accurate predictions. By the end of the course, learners will be able to design, train, and evaluate machine learning models using Python, and critically assess their performance in practical applications.

This course introduces students to Knowledge Representation and Reasoning, focusing on methods for encoding knowledge and reasoning under uncertainty. Students will learn to translate natural language problem statements into symbolic, logical, and graphical representations, including Bayesian networks and other probabilistic models. The course covers probabilistic reasoning, Bayes’ theorem, Bayesian inference, and techniques for evaluating conditional independence. Through practical examples and exercises, students will compute probabilities, analyse dependencies, and apply reasoning methods to real-world problems. By the end of the course, learners will be able to represent knowledge formally and perform informed, probabilistic reasoning to support decision-making in AI systems.

This course provides students with supervised industrial training through the Students Industrial Work Experience Scheme, giving practical exposure to real-world computing and Artificial Intelligence environments. Students are attached to organisations where they observe professional operations, carry out tasks, and acquire hands-on skills relevant to their field of study. The course emphasises workplace learning, skill development, and professional conduct. Students will maintain performance records, reflect on assignments completed, and submit a comprehensive report detailing their experiences and knowledge gained. This report is also formally defended to demonstrate understanding and practical competence in real-world computing and AI practices.

This course introduces students to the principles of Cloud Computing and its application in deploying scalable AI systems. Students will explore cloud architectures, service models (IaaS, PaaS, SaaS), and platforms such as AWS, Azure, and Google Cloud, with a focus on hosting and managing AI workloads. The course covers containerisation, orchestration with Kubernetes, and data pipeline management to ensure efficient, secure, and cost-effective AI deployment. Through practical exercises and case studies, students will deploy AI models as services, monitor performance, and evaluate trade-offs related to scalability, cost, and security in cloud-based AI solutions.

This course introduces students to the fundamentals of Autonomous Systems and Robotics, covering the design, operation, and application of robotic systems. Students will explore sensors, actuators, control systems, motion planning, and navigation techniques essential for autonomous system functionality. The course emphasizes practical experience using simulation tools and robotic operating systems (ROS) to model, program, and test robotic behaviors. Through lab exercises and mini-projects, learners will simulate robot motion, implement basic navigation algorithms, and apply perception techniques using sensors and cameras. Ethical, legal, and societal implications of autonomous systems are also examined, preparing students to develop responsible and effective robotic solutions across industries such as healthcare, agriculture, and service automation.

This course prepares students for Advanced AI Certification by focusing on professional-level competencies and career-aligned certification pathways. Students will explore advanced certification tracks for roles such as AI Engineer, ML Engineer, and Data Scientist, and analyse exam structures, performance-based assessments, and required skills. The course emphasises hands-on preparation, including using advanced AI tools, completing capstone-style tasks, and documenting practical evidence for certification. Ethical considerations, governance, and responsible AI practice are integrated throughout. By the end of the course, learners will be ready to attempt or complete a professional AI certification aligned with their career goals.

Peace and Conflict Resolution

Venture Creation

This course introduces students to the design and implementation of AI Search Techniques for solving complex problems. Students will learn to represent problem spaces, design heuristics, and apply both uninformed and informed search algorithms to a variety of tasks. The course covers state space representation, graph and tree traversal methods, and algorithmic strategies such as breadth-first search, depth-first search with iterative deepening, uniform-cost search, hill-climbing, greedy best-first search, and A* search. Advanced techniques, including minimax for adversarial games, genetic algorithms, and simulated annealing, are also introduced. Through practical exercises and problem-solving tasks, students will evaluate heuristics, implement search algorithms, and analyse their time and space complexities, developing the skills to tackle real-world AI search challenges efficiently.

This course provides students with a comprehensive introduction to Deep Learning, focusing on neural network architectures, training processes, and performance evaluation. Students will differentiate AI, machine learning, and deep learning concepts, understand how neural networks learn representations, and apply proper evaluation methods to ensure model reliability. The course covers foundational topics such as gradient descent, regularization, model ensembles, and the intuition behind deep feed-forward networks, convolutional neural networks, recurrent networks, transformers, and generative models. Students will also explore performance metrics, confusion matrices, cross-validation, and reinforcement learning basics. Through hands-on lab exercises using Python, learners will implement multilayer perceptrons, backpropagation, CNNs, transfer learning, and optimization techniques, enabling them to train, evaluate, and visualise deep learning models for practical applications in areas like vision, NLP, robotics, healthcare, and gameplay.

This course introduces students to the principles of AI-driven Innovation and Entrepreneurship, focusing on creating and managing technology-based ventures. Students will explore business models, feasibility studies, marketing strategies, and the startup process, with particular attention to opportunities in Artificial Intelligence and related technologies. The course covers innovation, product development, digital marketing, business strategy, and ethical and legal considerations in entrepreneurship. Through practical exercises, project presentations, and case studies of successful AI ventures, students will learn to identify opportunities, plan new ventures, and apply strategic and ethical decision-making in technology-driven business environments.

Data Management I

This course introduces students to Reinforcement Learning, focusing on the interaction between agents and environments to solve decision-making problems. Students will learn to model tasks using Markov Decision Processes (MDPs) and implement dynamic programming, Monte Carlo, and temporal-difference methods for learning optimal policies. The course covers policy iteration, value iteration, Q-learning, and SARSA algorithms, with applications in robotics, games, and recommender systems. Through hands-on lab exercises using Python and libraries such as OpenAI Gym, PyBullet, Gazebo, and AirSim, students will design, simulate, and evaluate reinforcement learning solutions for robotic and autonomous agent tasks, including multi-agent scenarios and real-world navigation challenges. Ethical and practical considerations are integrated into project-based learning to develop robust, responsible RL solutions.

Research Methodology and Technical Report Writing

This course introduces students to Computer Vision, focusing on methods for interpreting and understanding visual and sensory data. Students will explore image acquisition, processing, representation, and recognition, along with multimodal approaches including audio, touch, and proprioception. The course covers object recognition, image segmentation, motion analysis, feature extraction, and classification techniques, including statistical methods and deep learning approaches. Practical lab exercises using Python provide hands-on experience in image processing, transformations, enhancement, and implementing computer vision algorithms. By the end of the course, students will be able to develop, evaluate, and deploy computer vision solutions for real-world applications such as object detection, scene recognition, and multimodal sensory interpretation.

This course guides students through the first phase of their AI Final Year Project, focusing on identifying, planning, and proposing an AI research or development project. Students will learn to select a researchable topic, review relevant literature, and design a methodology tailored to their problem statement. Emphasis is placed on proper academic practice, including source referencing, data analysis planning, and coherent proposal writing. Students will develop a detailed project proposal and present it orally to their supervisor, demonstrating the ability to conceptualize, justify, and plan an AI project in a professional and methodical manner.

This course introduces students to MLOps and AI Systems Engineering, emphasizing the deployment, management, and maintenance of AI systems in real-world environments. Students will explore the principles of Machine Learning Operations (MLOps), learning how to design automated pipelines for model training, testing, deployment, and monitoring. Topics include version control for datasets and models, containerisation with Docker, orchestration with Kubernetes, continuous integration and deployment (CI/CD), and model serving via APIs and cloud platforms. Through case studies and practical exercises, students will evaluate scalability, reliability, and maintainability of AI systems, gaining the skills to integrate AI solutions effectively into enterprise and industrial applications.

This course introduces students to the Big Data Ecosystem for AI, focusing on managing and processing large-scale data to support Artificial Intelligence applications. Students will explore the characteristics of big data—volume, velocity, variety, veracity, and value—and learn about architectures and frameworks for handling it efficiently. The course covers distributed computing with Hadoop, MapReduce, and Apache Spark, as well as data storage solutions including HDFS, NoSQL, and cloud-based systems. Students gain hands-on experience with tools for data ingestion, processing, and analytics (Kafka, Hive, Pig, Spark SQL) and learn to integrate big data pipelines with AI workflows. Ethical, privacy, and governance considerations are emphasized, alongside case studies in healthcare, finance, and smart cities to illustrate practical AI-driven big data applications.

This course introduces students to the Virtual and Augmented Reality, focusing on immersive technologies and their applications. Students will explore the distinctions between Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), including the hardware and software components required to create interactive environments. Through hands-on labs and projects using platforms like Unity and Unreal Engine, students will design and implement basic VR and AR experiences, applying principles of user interaction, spatial computing, and immersion. The course also covers real-world applications in education, healthcare, industry, and entertainment, while addressing usability, ethical, and safety considerations. Students gain practical skills in integrating AI features into immersive applications, preparing them for innovative work in VR/AR development.

This course focuses on expert-level and specialist AI certifications, preparing students to align their professional development with industry and organisational needs. Learners will gain the skills to evaluate advanced AI certification paths, demonstrate mastery of professional competencies, and maintain ethical and compliant practices in AI. Students will also develop professional portfolios, document certification achievements, and plan long-term career growth, including credential stacking and strategic lifelong learning in AI.

Creative and Innovative Thinking for Founders

This course examines the interaction between AI technologies and society, focusing on ethical, societal, and practical implications of real-world AI deployments. Students will learn to formulate AI solutions to practical problems while evaluating the broader impact of their designs on fairness, privacy, trust, and human autonomy. Through case studies in areas such as healthcare, sustainability, education, robotics, and social media, learners will analyse algorithmic and evaluation bias, assess risks from deployed generative models (image and language), and explore societal, economic, and legal consequences. Ethical principles and responsible AI practices are emphasized, equipping students to design AI systems that are socially aware, accountable, and sustainable.

This course introduces the theory and practice of Natural Language Processing (NLP), focusing on how computers can interpret, represent, and manipulate human language. Students will explore both deterministic and stochastic approaches to grammar, parsing algorithms, and semantic representations, gaining a foundational understanding of linguistic structures and meaning. Through hands-on lab work and projects using Python, learners will implement NLP techniques for tasks such as text preprocessing, part-of-speech tagging, chunking, named entity recognition, and feature extraction with TF/IDF. Applications in information retrieval, language translation, and text classification are emphasized, alongside exposure to modern methods including n-gram models, probabilistic parsing, deep learning architectures, and multi-modal embeddings. By the end of the course, students will be equipped to develop robust, real-world NLP solutions while critically evaluating the accuracy, limitations, and applicability of different methods.

This course provides students with an opportunity to implement, evaluate, and finalise their capstone research projects in Artificial Intelligence. Building on the proposal and planning conducted in Final Year Project I, learners will apply technical skills to develop AI solutions, integrate methodologies, and perform rigorous testing and evaluation. Students will document their work comprehensively, producing a formal technical report covering the full project lifecycle, and will present and defend their findings before faculty evaluators. Emphasis is placed not only on technical competence but also on transferable skills such as communication, teamwork, and critical problem-solving. By the end of the course, students will gain hands-on experience in conducting independent research and delivering professional-grade AI projects, preparing them for industry or further academic pursuits.

This course explores the unique security challenges facing artificial intelligence systems, with a focus on adversarial attacks and defenses in machine learning. Students will gain an understanding of how malicious inputs can compromise model integrity and learn strategies to enhance the robustness and reliability of AI systems. Through a combination of theoretical study and practical exercises, learners will implement techniques to detect, prevent, and mitigate adversarial threats, balancing security requirements with model performance. The course also examines privacy-preserving methods, including differential privacy and federated learning, and addresses ethical, legal, and societal implications of AI vulnerabilities. By the end of the course, students will be equipped to design AI systems that are secure, resilient, and ethically responsible in real-world applications.

This course examines the development and application of machine translation (MT) technologies, providing students with a comprehensive understanding of rule-based, statistical, and neural approaches to automated language translation. Learners will explore the historical evolution of MT, study preprocessing techniques for text, and implement translation models using contemporary libraries and frameworks. Through practical exercises and evaluation tasks, students will assess translation quality with standard metrics such as BLEU and METEOR, while exploring challenges in translating low-resource and morphologically rich languages. The course also emphasizes ethical, cultural, and societal considerations, preparing students to deploy machine translation systems responsibly in multilingual and cross-cultural contexts.

This course introduces the principles and applications of quantum computing with a focus on Artificial Intelligence. Students will explore the fundamentals of quantum mechanics relevant to computation, including qubits, quantum superposition, entanglement, and quantum gates. The course provides hands-on experience in designing and simulating quantum circuits and implementing foundational quantum algorithms such as Deutsch–Jozsa, Grover’s search, and Shor’s factoring algorithm. Through practical exercises using quantum simulators (e.g., Qiskit or Cirq), learners will apply quantum computation techniques to simple optimisation and search problems, while evaluating the potential impact of quantum technologies on AI, cryptography, and computational efficiency. The course also examines the current limitations, noise challenges, and future prospects of quantum computing, equipping students to critically assess its integration into AI systems.

This course explores the integration of Internet of Things (IoT) systems with Artificial Intelligence, focusing on the architecture, devices, and data pipelines that enable intelligent, connected environments. Students will learn about sensors, actuators, embedded devices, and communication protocols that support data acquisition and transmission in IoT networks. Through practical examples and case studies, learners will examine how IoT-generated data can be processed, streamed, and analysed using AI platforms, enabling applications in smart cities, healthcare, agriculture, industry, and transportation. The course also addresses security, privacy, and ethical considerations, preparing students to design AI-driven IoT systems that are robust, responsible, and impactful.

This course introduces students to Agentic Artificial Intelligence, exploring intelligent agents that perceive, reason, plan, act, and learn within dynamic environments. Students will examine the architecture and components of both single-agent and multi-agent systems, understanding how agents interact, make decisions, and coordinate in complex scenarios. Through practical examples in robotics, software automation, decision support systems, and modern applications of Large Language Models as autonomous agents, learners will apply agentic AI principles to real-world problems. Ethical, safety, and governance considerations are emphasised, equipping students to design autonomous AI agents that are effective, responsible, and aligned with societal expectations.