Want to learn about more advanced programming constructs and techniques? Topics covered in this module include concurrency, distributed programming, design patterns, and others. We will also take a closer look at some of the programming concepts taught previously. The module features a substantial, non-trivial assignment that should help you to hone - and demonstrate - your programming skills.

As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges.

The world demands software systems with ever increasing richness of behaviours and degrees of complexity. However, traditional software engineering techniques, which were inherited with relatively minor adaptations from other, older branches of engineering, have been struggling to scale up to the challenges posed by modern software systems. As a result, a large proportion (as much as a quarter!) of software projects based on traditional methods end up being cancelled at some point in their lifecycle, with many more being late, over budget and with less features than initially stipulated. In this module you will learn why traditional software engineering techniques fail, and you will become very familiar (through lectures, labs, videos and a large group project) with a novel set of techniques, known as Extreme Programming and Agile Software Development, which fundamentally solve these problems. In the last decade, these techniques have been so successful that today as many as 80% of all projects adopt agile methods.

In a world increasingly driven by data, the need for analysis and visualisation is more important than ever. In this module you will look at data through the eyes of a numerical detective. You will work on the lost art of exploratory data analysis, reviewing appropriate methods for data summaries with the aim to summarise, understand, extract hidden patterns and identify relationships. You will then work on graphical data analysis, using simple graphs to understand the data, but also advanced complex methods to scrutinise data and interactive plots to communicate data information to a wider audience. For data analysis and visualisations you will use R-studio, and a combination of R-shiny applications and google visualisations for interactive plotting.

How do you apply an algorithm or numerical method to a problem? What are the advantages? And the limitations? Understand the theory and application of nonlinear programming. Learn the principles of good modelling and know how to design algorithms and numerical methods. Critically assess issues regarding computational algorithms.

In this module you will learn techniques underpinning algorithms for studying integer-valued systems, and apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms.

Are you interested in understanding how AlphaGo was able to beat a top Go player? In this module, you will learn about the models behind successful stories of Reinforcement Learning, where a machine (agent) makes sequential decisions to reach an optimal goal. The lectures will be complemented with Lab sessions where we will take advantage of the Open AI Gym environments, allowing us to train our agents to perform tasks such as playing videogames (Atari) and more.