Imperial College London

In this class, students will work to prove mathematical properties of graphs.We will explore classical algorithms associated with graphs and trees and design algorithms for sorting and searching. Students will apply various methods for determining the time complexity of algorithm and study the complexity classes P and NP and the concept of NP-completeness. Students will also work to prove basic properties of graphs. We will describe, and establish the correctness of, some of the fundamental algorithms in computing and analyze the time complexity of an algorithm. Students will be able to explain the complexity classes P and NP and the P=NP problem and determine to which complexity class a computational problem belongs.

The class provides students with an understanding of project management and its central role in the modern business organisation. It presents methods that are used to define project environments, such as organisational strategy and major stakeholders. Students will design project processes via time planning, cost and benefits planning, as well as risk management. Students will also execute projects, by considering elements of organisational theory, management and leadership, as well as project control.You will learn to improve project performance via organisational learning

This course studies networks and communications. It focuses on learning about applications of computer communications and types of data, channels, bit rate and network topologies, LANs, MANs and WANs, broadcast and point-to-point, and synchronous and asynchronous communication. Students also examine connection-oriented and connectionless communication; theoretical capacity of channels (Shannon’s law and the Nyquist relationship); multiplexing; queuing theory; computer communication system architectures; the OSI reference model; and the seven layer model.

In this course, students study groups (homomorphisms, isomorphisms, normal subgroups, and first isomorphism theorem); fundamental theorem of abelian groups, groups of small order, group actions, Burnside's lemma, and applications; linear algebra (direct sums, quotient spaces); determinants, minimal polynomials, and Cayley-Hamilton theorem; and canonical forms of matrices, Jordan normal form, and rational canonical form.

This class covers the fundamental concepts and advanced methodologies of machine learning for imaging and relates those to real-world problems in computer vision and medical image analysis. You will experience different approaches to machine learning including supervised and unsupervised techniques with an emphasis on deep learning methods. Applications include image classification, semantic segmentation, object detection and localisation, and registration. A key objective is to equip you with the skills needed to work in, and conduct research into, image computing and applied machine learning.

This course studies soils and engineering geology. Students design a geotechnical site investigation that accounts appropriately for the geological context; decide whether design problems involve drained or undrained loading; derive appropriate design parameters for soil strength, compressibility, consolidation, and permeability from laboratory test data; analyze simple stability problems for design; and predict flow rates and pore pressures in simple seepage design problems.

The course explores management of innovation and the skills needed to manage innovation at both strategic and operational levels. It provides evidence of different approaches based on real-world examples and experiences of leading organizations from around the world. Innovation is a fundamental driver of competitiveness and it plays a large part in improving quality of life. Innovation, and particularly technological innovation, is inherently difficult, uncertain, and risky, and most new technologies fail to be translated into successful products and services. Given this, it is essential that students understand the strategies, tools, and techniques for managing innovation, which often requires a different set of management knowledge and skills from those employed in everyday business administration.

On this course, students will learn the fundamentals of geometric nonlinearity for discrete structural systems. We will examine principles of stability and buckling analysis for discrete structural systems.We wil explore nonlinear solution procedures for tracing equilibrium paths and geometrically nonlinear finite elements for one-dimensional structural systems.We will also examine materially nonlinear finite elements for one-dimensional structural systems.