Computer Science

Computer science is a broad ranging and diverse discipline, with many distinct specialist areas. This course allows students to experience some of this breadth by allowing them to study two independent topics of their choice

As industries worldwide transition toward sustainable practices, this course provides an introduction to the intersection of sustainability and Industry 4.0. It focuses on the design and development of innovative products and services with the tools to link emerging technologies with ecological responsibility. By combining theoretical foundations with hands-on project work, the course equips students to understand how emerging technologies — including digital modeling, artificial intelligence, Internet of Things (IoT), renewable energy, and advanced materials — can be leveraged to address pressing environmental and societal challenges. The course centers on Sustainable Prototype Challenge, in which students working in teams design a forward-looking product or service concepts. Through intensive assignments and workshops, participants apply principles of sustainable development, user-centered design, and technology-driven workflows to create a coherent final project. Deliverables include both a project prospectus and demonstrative materials (e.g., diagrams, mock-ups, or digital prototypes) that communicate the innovation’s environmental, functional, and social contributions, as well as feasibility. Students reinforce their learning through lectures, group critiques, and interdisciplinary collaboration. The course actively integrates perspectives from engineering, business, and design, preparing students to work across disciplines. In addition to gaining technical and creative skills, participants strengthen critical thinking, problem-solving, and communication abilities that are transferable to professional contexts.

The course combines practice with theory, and polishes students’ programming skills solving problems by programming language, data structure, and algorithms.
 

This class focus on the fundamentals of Python programming and will cover variables, branching, loops, lists, 2D list, and dictionary. The applications of Python coding include image processing and csv file processing.

This course offers a study of the fundamentals of data protection and the principles of cybersecurity. Topics include cryptographic techniques to ensure the confidentiality, integrity, and authenticity of data, analysis of vulnerabilities, threats, and attacks in systems and networks, and design and implementation of appropriate defense, detection, and response measures. This course also explores the legal and regulatory frameworks related to data protection and how to apply them in practice.  

This course examines human behavior and humans' expectations of computers, computer interfaces and the interaction between humans and computers, the significance of the user interface, interface design and user centered design process in software development, and interface usability evaluation methodologies and practice.

This course teaches the foundational knowledge necessary to understand how modern AI systems work, with a particular focus on deep neural networks. It moves from first principles through to exploring core training algorithms that power today’s models. 

Students will construct deep networks, image classification models, and generative models such as autoencoders as well as consider how transformer architectures enable large language models such as ChatGPT.

The course begins with an introduction to the basics of programming in Python, in particular understanding object-oriented programming and its importance to deep learning. The course quickly proceeds to an introduction to artificial intelligence, examining the fundamentals of supervised machine learning, including linear regression, logistic regression, neural networks, and gradient descent. In the second week, students explore image processing, investigating transformations, convolutional filters, and edge detection, before an introduction to convolutional neural networks and some prominent CNN architectures such as VGG and ResNet. In the final part of the course, students look at the core concepts of natural language processing, including sequence modeling, autoregressive models, and recurrent neural networks.

This course covers the engineering concepts required to build robust and trustworthy systems that make use of machine learning. Students look at all aspects of systems, from data ingestion to user experience, while considering the influence of regulation and wider society. Students spend half of their time in lectures, and half in the lab. Students implement and operate a simplified machine learning based system in a simulated environment inspired by a real-world problem. The course does not cover the design of machine learning models themselves, and students focus on the systems that surround and support them.

This course focuses on the general culture of artificial intelligence. It teaches the basics of logic for asking and resolving complex problems, obtaining knowledge, using that knowledge, through applying intelligent systems (chat bots, moving through the system), applying and building upon knowledge. It includes an introduction to AI coding.