Imperial College London

In this course students are introduced to the design principles of transport systems, their application to the movement of people and goods, within the wider context of the civil engineering profession. The course covers concepts such as travel demand, value of time and the environmental of transportation, underpinned by mathematical programming and algorithmic techniques. A strong systems design thread is running throughout the course, which includes aspects of network design, optimization, and performance maximization.

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.

In this course students explore how to conduct academic integrity research, considering topical areas such as what factors cause students to cheat, and how well university interventions to the misuse of generative AI work. The course also explores using AI to support the academic research process. It provides transferable research skills that can be used in other courses. The course is taught through a mixture of in-person, online, and prepared videos and resources. It also features guest presentations and discussion-based sessions, which are held periodically during the course. At the end of the course, students present their research as part of a group at an online external conference.

In this course, students develop essential engineering skills, working in groups and using problem-based learning to find solutions to real engineering projects; are inspired and be given the purpose behind the method of turning theoretical teaching into practice; work as a group on the development of conceptual designs and to be a creative “designer”; generate ideas and carry out research to assess the feasibility of design concepts; experience hands-on modelling as ways of communicating ideas; work as a group to plan and manage the construction of a design at a 1:1 scale; develop application of geometric and parametric modelling in design; give and receive critical feedback on designs responses to the developed brief; and reflect on the role of civil engineers as designers and makers. The course is delivered across two intensive weeks during Creative Design where no other teaching takes place. 2 x 1 weeks are Creative Design weeks in which students work as groups to develop civil engineering designs across multiple design scales.

The course offers students a strong foundation in the theoretical side of computer science. The course explores the skills involved in dealing with abstract systems and constructing proofs, through both teaching and exercises. Students learn to construct rigorous arguments about properties of the Lambda Calculus, construct reduction sequences according to particular reduction orders, and understand the relationship between the Lambda Calculus and other notions of computability.

The course focuses on formal descriptions (models) of computational behavior. Students learn about the operational semantics (formal description) of a simple 'WHILE' programming language; the operational semantics of other styles of real-world languages, such as Java and Haskell; and equivalent definitions of algorithm, initiated in the 1930s and providing the foundations for programming languages and computation.

How do you go from idea to business? This entrepreneurship course offers students a unique opportunity to build knowledge, skills, and experience in starting up a new business. Students gain a behind-the-scenes perspective into the entrepreneurial world through lectures, project work, and guest lectures. The course inspires students to develop an entrepreneurial mindset when setting up and driving new projects or when building a businesses. 

This course focuses on the theory and practice of concurrent programming, in order to familiarize students with a range of modern concurrent programming models, and with the challenges of correct concurrent programming. It is divided into two parts: a largely practical part that gives an overview of shared memory concurrency in modern programming languages, and a more theoretical part that treats shared memory concurrency from the ground up, covering low-level topics such as hardware memory models, primitives for shared-memory concurrency, building up towards higher-level synchronization techniques such as locks, mutexes and transactional memory. Throughout the course, a main objective is to reinforce correct concurrent programming through the use of tools, formal specification and verification techniques.

Performance engineering is the area of computer science ensuring that computer systems (comprised of hardware as well as software) are responsive, scalable, and efficient.  The course introduces fundamental principles and techniques used in performance management of modern computer systems, either purpose-built applications or generic system (single-node as well as distributed). 

This course presents the systems and hardware foundations of modern machine learning, bridging neural network workloads, compiler optimizations, and advanced AI hardware. The course covers cutting-edge AI hardware, including GPUs, TPUs, and wafer-scale accelerators, highlighting the design trade-offs between cloud and edge deployment. Topics such as efficient ML (quantization, pruning), algorithm–hardware co-design, federated learning, and system support for large language models (LLMs) are also presented. Through lectures, tutorials, and hands-on programming lab sessions, students gain practical skills and a systems-level understanding of ML deployment, preparing them for advanced research or industry roles in ML system design.

This course addresses the fundamental concepts and advanced methodologies of deep learning and relates them to real-world problems in a variety of domains. It provides an overview of different approaches, both classical and emerging. The course equips students with the necessary knowledge and skills to work in the field of deep learning and to contribute to ongoing research in the area.