Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology.

Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.

We live in an era in which the amount of information available in textual form - whether of scientific or commercial interest - greatly exceeds the capability of any man to read or even skim. Text analytics is the area of artificial intelligence concerned with making such vast amounts of textual information manageable - by classifying documents as relevant or not, by extracting relevant information from document collections, and/or by summarizing the content of multiple documents. In this module we cover all three types of techniques.

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.

This module provides an introduction to the econometric methods commonly used to analyse economic data and the application of these methods. The focus of the module is on linear regression and the ordinary least squares (OLS) estimation procedure. This is introduced after a review of basic probability, random variables, simple descriptive statistics and the concept of the null hypothesis.

Wish to conduct your own research using econometric methods? Understand econometric methods and learn to apply them to a wide variety of situations. Examine methods of linear regression and hypothesis testing. Study time series concepts of unit roots and co-integration. Explore ideas around model selection and panel data models.

Get to grips with modern microeconometrics. Examine the nature of causal and noncausal models, discuss the nature of microeconomic data structures, and review core econometric methods in the first part of this module. Then, analyse limited dependent variable models, including discrete choice and selection models, and examine program evaluation methods.

What are the main issues with panel data? And the main econometric techniques to analyse panel data? What methods can you use to evaluate spell duration data? Answer such questions with examples from labour economics, while gaining the skills to analyse a variety of research and policy problems.

What are the issues regarding different types of panel datasets? Or problems with survey methodology? Understand longitudinal data analysis by using micro-econometric techniques and critically examine survey methodology issues, like response rate and sampling frames. Apply panel data methods to study labour markets, focusing on marriage, unemployment and wages.

Theories of justice are still being worked on and developed today. You question contemporary theories of justice through applying them to some of the most controversial issues dominating contemporary politics.

Study one of the most important contemporary aspects of political action: the natural environment. You consider the state of the environment and possible paths along which it might change, before exploring environmental policies from the level of individual values to the environmental movement to political parties, and finally to the level of international affairs.

This course is about how representative democracy works in Europe. We will examine several topics within the European context, including: public opinion, political participation, political parties, electoral systems, party competition, and how to evaluate democracies. We will also develop specific knowledge about several European countries, by learning how the political institutions (several are mentioned above) function within them. The course also provides an accessible introduction to research design and methods that political scientists have used to address these topics.

Master the quantitative methods that are essential for testing hypotheses. You will study hypothesis testing, linear regression models, and more advanced regression models ubiquitous in political science, accompanied by data science and R programming skills.

In this module you focus on conflict resolution in inter- and intra-state issues. You gain experience in the practical as well as in the theoretical aspects of negotiation and mediation, exploring the applicability of various tools and techniques in problem-solving real cases of international conflict, and making use of negotiation and mediation techniques in role playing exercises and other types of simulations.

In this module you focus on conflict resolution in inter- and intra-state issues. You gain experience in the practical as well as in the theoretical aspects of negotiation and mediation, exploring the applicability of various tools and techniques in problem-solving real cases of international conflict, and making use of negotiation and mediation techniques in role playing exercises and other types of simulations.

This module presents advanced quantitative methods for political science based on maximum likelihood estimation (MLE), with a particular focus on the generalised linear model (GLM). After introducing the principles of MLE, models for different kinds of outcome distributions, such as binary, ordinal, categorical, count, and event history data, are considered. This module also introduces some advanced methods beyond the GLM. All models and methods are approached substantively, mathematically, and computationally (using R), with applications to political science research questions. Throughout the module, you will also familiarise yourself with the interpretation and presentation of empirical evidence in political science. The module will be particularly useful for you if you aim to pursue a career in academia or in research-intensive environments, for example think tanks, research-related government posts, data science, or survey analytics.

How do you critically analyse quantitative data? What are the appropriate statistical techniques for your research questions? And how do you interpret your results? Learn to conduct investigations relevant to your own research, as well as be a critical user of other research.

How do you interpret studies using panel data? What are the various approaches to panel data analysis? And can you analyse the same data using different methods? Gain the knowledge and confidence to manipulate panel data sets, while developing practical skills in selecting and conducting panel data analysis.