Imperial College London

This course builds on previous stress analysis courses by extending the concepts of linear elasticity to two and three dimensions, as the basis for advanced stress analysis. Topics covered include complex stresses and strains, Mohr’s circle, failure criteria, shear stresses in beams, thick-walled cylinders, plastic failure and buckling of struts. The course enables students to develop sufficient familiarity with stress analysis and strength of materials to design a safe and reliable load-bearing component of simple geometry (or to assess the safety of an existing one).

This course teaches the fundamental laws of thermodynamics and how they can be used to solve a range of simple engineering problems. The pace of the course takes account of students' lack of familiarity with the subject from pre-university studies. The aim of the lectures and tutorials is to develop analytical skills and some design appreciation, involving awareness of the interaction between thermodynamics and considerations of energy resources, materials, solid mechanics, economics, the environment, etc.

This course reinforces students' previous knowledge of stress analysis, and extends this knowledge to more advanced theories and techniques, and to apply these to practical problems. Most of these are developments of methods which have been previously acquired but to more sophisticated problems. New areas of thermal stresses, plastic deformation and residual stresses are treated and a new technique of analysis using energy methods is also introduced and developed. 

This is a project-based course where students work in a team to carry out the development and management of a relatively large scale software project, building a piece of software to fulfil the needs of a particular customer. Students put into practice state-of-the-art techniques used in industrial software development to ensure that their team produces software cooperatively, reliably, and on schedule. Each team works on a different project, and receives individual coaching to provide support and advice relevant to their particular project.

In this course, students study the principles of computer networking, analyze and discuss the OSI & TCP/IP models, demonstrate how a network is designed based on specific requirements, and learn basic principles of computer security.

This course examines kinematics and kinetics of human locomotion, bone, and soft tissue failure, macro- and micro-circulatory mechanics in various organs, and practical approaches to quantifying biomechanics. It describes how mechanics plays a role in basic physiological processes in the human body, as well as employing kinematic and kinetic principles to describe human locomotion. The course explores failure mechanisms of bone, as well as the differences between macro and microcirculatory flows. Students examine mass and fluid transport mechanisms in physiology.

This course explores debates around collective intelligence and follow the evolution of the group-mind from past to present and into the future. It looks at how the act of thinking together can go wrong in paranoid conspiracy theories, information bubbles and market panics and how, perhaps, it might be done better.

In this course, students complete a long-term individual project in order to demonstrate independence and originality, to plan and organize a large project over a long period, and to put into practice knowledge, skills, and research methods. Students are able to submit an original proposal, or browse from projects proposed by prospective supervisors.

This course guides students through the “exciting nightmare” of taking an idea or a technology to market, growing the venture, and securing a successful exit. Although grounded in rigorous theory, the focus of the course is highly practical and class participation is actively encouraged. No prior knowledge of the subject is required but students should be interested in the creation of wealth and the commercialization of technology. 

This course introduces students to the core ideas and fundamental concepts behind machine learning. Students learn different machine learning problems and the algorithms that exist to address them. They formulate machine learning problems and machine learning pipelines, apply suitable algorithms to tackle different machine learning tasks, implement machine learning algorithms to solve supervised learning problems, and assess appropriate methodologies to evaluate machine learning algorithms.