Artificial Intelligence (with a Year in Industry) BSc (Hons)

The first year serves as an introduction to artificial intelligence blended with foundational computer science principles. The year emphasises both theoretical understanding and hands-on experience, ensuring students are well-prepared for the AI-centric challenges ahead.

Students will engage in programming using Python and C, two languages pivotal to AI development. These languages not only facilitate software creation but also nurture computational thinking, a cornerstone of AI problem-solving.  Course topics include:

• Professional Skills: Tailored to set the stage for learning about computing and AI and ensuring you have the necessarily study skills.
• Structured Problem Solving: With Python as the primary tool, students will learn to approach problems methodically, a skill crucial for AI algorithm development.
• Database Technology Fundamentals: As AI often relies on vast datasets, understanding database technology from theory to practice is essential.
• Python Programming: A deep dive into Python, the go-to language for many AI applications.
• Introduction to Artificial Intelligence: An overview of AI, its history, applications, and the potential it holds for the future.
• Website Development Basics: This basic understanding is essential for all students, going into HTML, CSS and the basics of Javascript.
• Data Engineering Foundations: Learn the intricacies of handling, processing, and analysing data, a critical component in training and refining AI models.

Throughout this first year, students will gain a holistic understanding of the AI landscape. By engaging in practical Python and web programming projects, they'll apply their theoretical knowledge, honing their computational problem-solving skills. This robust foundation ensures they're well-equipped for deeper dives into artificial intelligence in subsequent years.

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

• Intelligent Systems: 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 and Study Skills: The essential skills that every AI and computer science professional should have. This topic emphasizes 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: The intricacies of algorithm development and performance analysis are explored. Learn how to design efficient algorithms, understand their complexities, and choose the right algorithm for specific tasks, optimizing both time and space.
• 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.

You spend your third year on compulsory placement in industry.

This year focuses on advanced and specialized 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 (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, followed by advanced methods like Transformers and Language Models in the second semester. The course also covers the application of these techniques in areas like Stock Trading, preparing students for roles in high-earning fields like Data Science and Search Engineering.
• Vision Systems: This builds on previous knowledge of computer vision, focusing on more advanced techniques 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): Complementing your AIoT studies, you will learn about the principles and applications of interconnected devices and systems. The course covers the fundamentals of things such as smart devices, sensor networks and data processing.
• Cybersecurity: You will study topics such as network security, cryptography, and threat detection. Students learn 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.