Imperial College London

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.

Students explore quantitative mathematical methods for taking decisions in the presence of constraints or finite resources; learn about linear programming, integer linear programming, robust optimization, and game theory and their application; classify mathematical programs on the basis of the number and types of their solutions; implement solution techniques for linear programs with both real and integer-valued variables; and become familiar with fundamental notions of duality, degeneracy, and sensitivity.

This course builds on the foundations set in the first year by further exploring the business environment in which civil engineering projects take place. It is part of the design thread through the MEng program. The course introduces the concept of risk management and the different types of risks inherent in the civil engineering industry. It also gives an overview of the organizations involved in the supply chain and the principles of project management and procurement, including ways to improve performance, worker safety, and the environmental impact.

In this course students have the opportunity to explore this exciting topic that sits at the boundaries of computer science and physics. The course is taught from a computer science perspective, but it also draws on topics from linear algebra, which provides the mathematical apparatus for formalizing quantum systems. Students are introduced to the basic notions of quantum computing, including quantum bits and quantum entanglement and explore quantum algorithms, such as Quantum Search, Quantum Simulation and Quantum Information concepts such as quantum error correction.

In this course students learn about fundamental soil mechanics and geotechnical engineering; analyze soil origin, composition and variables used to classify soils and assess their state; learn basic concepts related to the one-dimensional flow of water through soils; explain the purpose and basic design principles of building foundations and retaining walls; become familiar with soils, rocks, and the use of geological maps and sketches in desk studies; appreciate the importance of developing a detailed understanding of ground conditions in successful civil engineering; and recognize major geotechnical hazards, plan appropriate site investigations, and develop safe and successful geotechnical design. 

The course teaches students on modelling, analysis, and design methods in relation to continuous and discrete control systems. Introduction to state variable analysis is also provided. It is assumed that students have some knowledge of classical control theory, including frequency response methods and complex frequency methods. A level of understanding of linear algebra is also assumed. The coursework assignments are designed to give the student an opportunity to develop skills in carrying out realistic control designs using modern simulation and analysis tools.

The widespread adoption of deep learning methods has been largely driven by the availability of easy-to-use systems such as PyTorch and TensorFlow. However, it is less common for users to explore the internals of the libraries and understand how they function, as well as how to optimize the high-level code for hardware systems. When deep learning algorithms are deployed into custom hardware, they are often modified to run faster and more efficiently. This course provides students with the basic concepts and principles of modern deep learning systems, and it explores how optimizations can be applied from both the software and hardware aspects of the system stack.

The major processes involved in energy and environmental engineering are of an interdisciplinary nature. Understanding these important processes requires a good knowledge of the fundamental science in disciplines such as microbiology, chemistry, and thermodynamics. This course provides students with an overview of these disciplines with the focus on the application to a wide range of energy technologies and the environment.

This course develops students' understanding of the fundamental principles needed to design drinking water supply and treatment works, wastewater collection and treatment systems, and their ability to develop waste and resource management strategies. 

This course is driven by time series perspectives and short term statistical behavior, underpinned by digital signal processing and machine learning, which is appropriate for cash assets (stocks, bonds, currencies, portfolios) and futures. It therefore answers the needs of the rapidly changing global financial system which requires financial models to be adaptively inferred from the data. Students gain hands-on experience through structured Python assignments based upon time series models, robust estimators, subspace techniques, portfolio optimization, and data analytics on graphs. There are no particular prerequisites, although knowledge of estimation theory and statistics would be useful.