Artificial Intelligence BSc (Hons)

This is a broad introduction to the subject and you develop the theoretical knowledge, problem solving and practical skills that underpin AI:

• Introduction to Programming: This module explores the foundational concepts of programming and data structures, focusing on Java and Python, and examines how skills in structured coding, object-oriented programming, and core algorithms support the design of efficient, maintainable solutions to computational problems.
  
  
• Introduction to Artificial Intelligence: In this course, we will explore the field of Artificial Intelligence (AI), starting with foundational concepts and progressing through its diverse applications and implications. We begin by understanding what AI is, its history, and core principles such as machine learning, neural networks, and natural language processing. The course also covers key AI techniques such as supervised and unsupervised learning, data analysis, and algorithm development. We will look at the applications of these techniques in engineering and real-world scenarios. In addition, we will examine the ethical and societal impacts of AI technologies, particularly in fields such as automation and data privacy. Throughout, we will link AI concepts with engineering mathematics and applications and differential equations to enhance problem-solving approaches.
• Data Fundamentals: The Data Fundamentals module introduces the concept of data and its collection, processing, analysis, and interpretation, while exploring storage systems such as relational databases and the end-to-end lifecycle of data in real-world contexts.
  
  
• Fundamentals of Computational Science: This module introduces the foundations of computer science by weaving together mathematics, C programming, cryptography and scientific computing. Students begin with sets, logic, and proofs to build the habits of abstract reasoning and formal problem-solving. These mathematical tools naturally flow into programming, where they gain practical competence in C, learning to manage variables, control flow, memory and debugging while building an appreciation for efficient and reliable code. The curriculum expands into applied domains, showing how mathematics and programming underpin security and scientific analysis. Students implement and break classical ciphers, apply public-key principles and tackle numerical methods such as matrix operations, integration and error analysis. Integration is central, in that through collaborative projects and simulations, students see how these themes combine to support secure, optimised and interdisciplinary problem-solving across computer science.

During your second year, you will build upon the foundational knowledge from the first year. Topics include the following:

• Machine Learning: You will gain a wide range of skills in AI, with an emphasis on machine learning, but also metaheuristics and cellular automata. Understand foundational ML algorithms, understand neural networks, reinforcement learning and deep learning. You will also explore the ethics of AI, the nuances of NLP, and the potential of semi-supervised learning, ensuring a well-rounded grasp of intelligent systems.
  
  
• Graph Theory: Understanding the mathematical foundations of graphs is essential to modern machine learning, notably current edge techniques such as graph neural networks.
  
  
• Computer Vision: You will study how machines interpret and understand visual information from the world. Learn the foundational techniques and algorithms that enable computers to process, analyse, and make decisions based on visual data, bridging the gap between human and machine perception.
  
  
• Professional Skills: The essential skills that every AI and computer science professional should have. This topic emphasises effective communication, teamwork, and the critical study techniques that will support your academic journey and future career.
  
  
• Object-oriented Programming with C++: This topic provides a comprehensive understanding of classes, objects, inheritance, polymorphism, and other core OOP concepts, ensuring a strong foundation for advanced software development.
  
  
• Algorithm Design and Analysis: Understand the heart of computational problem-solving. This course will introduce you to the design, analysis, and implementation of algorithms, ensuring you can develop efficient and effective solutions to complex problems.
  
  
• Introduction to Software Engineering: Master the principles of software development, from requirement analysis to deployment. This topic covers best practices, design patterns, and methodologies that ensure the creation of robust, scalable, and maintainable software systems.
  
  
• Human-Computer Interaction: Discover the science behind user-friendly interfaces and impactful user experiences. This topic focuses on design principles, user testing, and the psychology of user interactions, ensuring that software meets the needs and expectations of its users.

This year focuses on advanced and specialised areas of AI, providing students with in-depth knowledge and practical skills. The curriculum is designed to deepen understanding and enhance proficiency in several key domains of AI, preparing students for industry demands and innovations.

• Natural Language Processing Fundamentals (NLP): A critical component of AI, used extensively by companies such as Google, DeepMind, and Microsoft. Students will learn classical NLP techniques based on linguistics in the first semester.
  
  
• Applicant of Natural Language Processing (NLP): Advanced methods like Transformers and Language Models are studied in the second semester with the application of these techniques in areas like Stock Trading, preparing students for roles in high-earning fields like Data Science and Search Engineering.
  
  
• Convolutional Neural Networks: This builds on previous knowledge of computer vision, focusing on more advanced techniques, with a focus on CNNs, and applications for interpreting visual data. Students will study advanced object recognition and scene understanding, gaining skills applicable to robotics, autonomous vehicles, and augmented reality.
  
  
• Machine Learning Hardware: Also known as AIoT, this focusses on the practical aspects of AI, specifically on programming with PyTorch and deploying AI models to physical devices. Students will learn the essentials of hardware-software integration and the optimization of AI models for real-world deployment.
  
  
• Internet-of-Things (IoT): This module introduces you to the interconnected world of IoT. You'll learn how everyday objects can communicate over the internet and will have access to modern tools to develop and test your ideas.
  
  
• Cybersecurity: You will study topics such as network security, cryptography, and threat detection. Students loan to identify vulnerabilities, implement security protocols and learn how machine learning techniques can be used to predict and mitigate cyber threats. Practical sessions often involve hands-on labs and projects to apply theoretical knowledge in real-world scenarios.