Imperial College London

This course explores aspects of electrical power in the context of a wider energy system. The first aspect is electronic “switch-mode” circuits for conditioning and converting power such as in the grid interface for solar and wind energy systems. Circuits for various types of conversion between DC voltage levels and to/from AC are analyzed in order to support circuit design to meet a performance specification. The second aspect covers the electromagnetic devices such as transformers, motors, generators, and transmission lines of an AC power systems. The performance and efficiency of each type of electromagnetic device will be analyzed. The link between these devices and how frequency and voltage of a power system are controlled will be illustrated. The course concludes with a discussion of the transition needed in power systems to achieve zero carbon dioxide emissions and the roles of power electronic, electromagnetic, and information technologies have in that transition.

This course gives students a thorough grounding in electromagnetic systems in electrical engineering. It teaches students how electromagnetic systems provide the foundation of understanding and designing systems as diverse as electrical motors to wireless communication. The Maxwell equations are the basics of Electromagnetism. Students use vector calculus to solve these equations and apply them in low frequency and high frequency applications. Low frequency applications forms strong links with analogue and power electronics whilst high frequency application covers communications and sensing.

This course takes students through the idea of transmitting information from one point to another in the presence of noise. The course introduces students to both analogue and digital transmission, show how the two are connected, and explain the differences (e.g. signal-to-noise ratio vs. bit error rate). The course also introduces students to information theory, and the fundamental theoretical limits of compression and channel coding it identifies.

This course enables students to learn about and engage with engineering in the context of global society and within the engineering industry. Students apply this knowledge to create and plan initiatives, gaining an understanding of being an EDI champion and improving interpersonal skills. This course will provide students with the knowledge and critical understanding of the key issues surrounding equality, diversity and inclusion in engineering, STEM and wider society and to identify and evaluate actionable methods of embedding EDI into engineering education and/or industry

This course enables students to understand one of the core purposes of finance: the setting of prices in a market. The first half of the course works at a market-level, dealing with risk-management and diversification. The second half of the course works at the security-level, thinking about bond, stock, and derivative valuation.

This course explores how machine learning addresses the problem of how computers can learn and extract information automatically from data. It further explores the methods used in artificial intelligence, data mining, and adaptive system design. Students learn how machine learning is used in most disciplines where data is available, including, e.g., electrical engineering, computer science, or medicine. The course introduces students to the theory and practice of modern machine learning methods.

This course presents students with a comprehensive introduction to advanced topics in Linear Algebra as needed in the more advanced literature on Signals, Signal Processing, Systems and Control. The emphasis is on fundamental notions related to vector spaces, inner product spaces, normed spaces, matrix algebras, and computations with matrices.

This course begins by prompting students to reflect on what happiness means to them. What is happiness? What is it for? Does it matter? How can we measure it? The course explores a variety of ‘theories’ of happiness, asking students to think about what happiness means to them in their lives and communities. Through discussions, research, and reflection students are exposed to different interpretations and understandings of happiness. 

The course offers students the opportunity to apply their knowledge in a major group design project which considers the full design process; from the client brief to the demonstration of a prototype. You must work systematically from high-level goals to detailed design, drawing upon knowledge and skills learned in other courses. Communication with the client occurs throughout and involves an assessed presentation, report, and demonstration. Team-working is crucial, as groups must develop several subsystems in parallel and integrate them together, as well as carry out non-technical tasks such as documentation and cost management. In addition, students develop problem solving skills, project and time management, and communication.

This course introduces students to the fundamentals of deep learning and illustrates how it is contributing to the practical design of intelligent machines. Deep learning is currently the most active area of research and development and in high demand for experts by hi-tech start-ups, large companies as well as academia. It is the preferred approach for modern AI and machine learning in any domain. This course demonstrates how deep learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalization, robot control, time series forecasting, and much more.