Imperial College London

This course explores the basic equations that govern mass and momentum transfer of incompressible fluids as well as of important modes of heat transfer, for instance by phase-change (including boiling and condensation). By building on the fundamental aspects of the subject, problems are considered for a number of settings relevant to engineering applications.

This course examines network and web security broadly from the network to the application layer. The emphasis of the course is on the underlying principles and techniques, with examples of how they are applied in practice. Students study the themes and challenges of network and web security, and the current state of the art. They develop a critical approach to the analysis of network security and web application security, and learn to bring this approach to bear on future decisions regarding security.

This course explores the main algorithmic design paradigms and teaches students to apply algorithmic techniques to practical and unseen problems. Students quantitatively analyze the performance of algorithms. They also model the mathematical structure of computational tasks and apply the right algorithmic tools on them, and develop their algorithmic thinking and problem solving skills.

This course focuses on aspects of managing and leading organizations. Students learn how to influence and motivate others to get cooperation for their own goals. Topics include negotiation, leading and managing teams, motivation, and personality. The course also explores the organizational systems that coordinate individual work to meet business objectives and the impact of technology (e.g., artificial intelligence) and new business models (e.g., gig economy) on work.  

In this course, students use advanced mathematical methods to establish convexity in complex problems. In addition, students specify necessary and sufficient conditions for optimality, classify optimization algorithms as first or second order, determine appropriate optimization algorithms for given problems given the size and structure of the optimization models, and apply sensitivity analysis to optimization problems using Lagrange multipliers.

This course builds upon the knowledge and understanding gained by the students in the Separation Processes 1 course. This is  achieved by both broadening the content to encompass a wider range of separation processes and deepening the student’s understanding of the processes covered in Separation Processes 1. This is primarily achieved by building upon knowledge of distillation and extraction processes and design, introducing more complex variables and via the introduction of new separation processes such as adsorption. 

This course provides a comprehensive overview of the key contemporary issues in global economics and the leading models deployed by global economics institutions such as the WTO, United Nations, IMF and World Bank, as well as by global companies.

The course provides an overview of current applications of formal methods to modern AI systems, including the verification, certification, and monitoring of agent-based systems and solutions. The course is structured in four parts. The first part focuses on basic modal logic, its syntax and semantics based on Kripke frames and models, as well as the model theory of modal logic, including the notions of satisfaction and validity. The second part gives students the foundations for the specification of strategic behaviors of agents in multi-agent systems (MAS), including the formalism of Concurrent Game Structures as a mathematical representation of MAS. In the third part, students consider the role played by formal methods in modern AI systems, specifically reinforcement learning (RL). In the fourth part, the course introduces the language of Alternating-time Temporal Logic (ATL), an extension of the temporal logics CTL and LTL, which allows students to express game-theoretical notions such as the existence of a winning strategy for a group of agents. 

This course explores the advanced mathematical techniques required to understand, design, and implement modern statistical machine learning algorithms and inference mechanisms.

In this course, students learn to explain the behavior and properties of fluids (static and dynamic), solve problems involving incompressible flows, and apply these basic principles in flow measurements and other flow (e.g. pipe) related problems, and (ii) to develop a basic understanding of conductive, diffusion and convective heat and mass transport, emphasizing first principles analysis, and apply it to a broad range of contexts.