University of Bristol

Computer science is a broad ranging and diverse discipline, with many distinct specialist areas. This course allows students to experience some of this breadth by allowing them to study two independent topics of their choice

This course is team-taught so that each topic can be led by an expert in the field. The specific topics vary from year to year, but may include such things as film, photography, graphic novel, book culture and digital texts, street art, performance art, painting, social media, animation, monuments & memorialization, and music videos. Each topic introduces students to a specific approach, theory, or methodology that is well-suited to analyzing the particular sites, texts, or images for that topic.

The course introduces and explores technologies relating to high performance computing, and it offers practical hands-on use of and experience with said technologies. Students learn how to develop fast, efficient applications on the very latest advanced processors, including many-core CPUs and GPUs. Students also have the opportunity to integrate content from other courses, for example implementing high performance parallel versions of algorithms previously encountered. Students are exposed to the underlying trends in computer hardware that are driving development towards massive parallelism in hardware and software. They employ widely used parallel programming languages and tools, such as OpenMP, MPI, OpenCL, debuggers and profilers, all in the context of a real supercomputer environment: the university’s Blue Crystal cluster.

Students learn about the major mechanisms for the protection of human rights in the British Constitution and the role that judges have in enforcing human rights and administrative law. As such, students learn about the Human Rights Act 1998, the rule of law as a constitutional value, the process of legal accountability known as judicial review and relevant human rights topics, including but not limited to freedom of expression, the law governing protest in the United Kingdom, freedom of religion, and other relevant human rights topics. This involves detailed consideration of key pieces of primary legislation and international treaties relevant to human rights protection as well as important case law relevant to these topics. Students also consider and engage with many of the ongoing academic debates about these topics.

This course introduces a range of advanced topics in computer architecture, focusing mainly on high performance processors but including recent or hot topics (e.g., low-power design, wide vector units etc.). Students learn how various design decisions can improve quality (according to a metric such as performance, area or power consumption) which extends the output of previous units that focus mainly on functionality.

This course, covering financial accounting, management accounting, and investment appraisal topics, enables students to understand and apply the basic techniques of accounting and investment appraisal that would serve them well in decision-making roles in their careers ahead. The first part (Financial Accounting) provides an introduction to the preparation and analysis of financial statements and evaluation of the financial well-being of a business. The second part (Management Accounting and Investment Appraisal) provides an introduction to the production and uses of financial information for the internal management of a business. Students learn how to apply and comment on a range of techniques for product costing and investment appraisal.

Machine learning is concerned with algorithms that process relevant data and then perform some task. Often, performance of machine learning algorithms is measured statistically, and the algorithms themselves are heavily influenced by statistical ideas. For example, after observing several (x,y) pairs an algorithm may be able to predict with high accuracy the corresponding value of y for an unseen x. When the data is complex and/or high-dimensional, a number of statistical and algorithmic issues arise: a sufficiently rich class of statistical models must be used effectively and irrelevant data should be identified and then discarded. Students understand the statistical approach to analyzing data, and how it can be used to effectively perform tasks under appropriate assumptions. This enables students to formulate various real-life problems as statistical learning tasks and use common techniques to develop solutions.

A wide range of phenomena from areas as diverse as physics, economics, and biology can be described by simple probabilistic models. Often, phenomena from different areas share a common mathematical structure. In this course a variety of mathematical structures of wide applicability is described and analyzed. The emphasis is on developing the tools which are useful to anyone modelling applications, rather than the applications themselves Students should have a good knowledge of first year probability and of basic material from first year analysis. As the course builds on Probability 1, it also deepens students' understanding of the basis of probability theory.

This course provides an introduction to the fundamental approaches of cognitive psychology, biological psychology and the intersection of the two fields. It covers concepts relevant to phenomena such as attention, perception, language, memory, vision, emotion, and the neurophysiological processes involved in these. The course covers concepts relevant to brain functioning from chemistry, biology, cognitive science, and neuroscience, to explore how biological and cognitive explanations are complementary.

Topics covered in this course include writing and debugging software for hardware, working with microcontrollers, sensors, motors, key theoretical concepts for robotic systems, the scientific method applied to robotic systems, and scientific reporting and writing.