Computer Laboratory

Course pages 2016–17 (still under preparation!)

MPhil ACS

Please check the official timetables for timetable details.

Michaelmas term

  • Advanced Operating Systems (L41) – Dr Robert Watson – 16 h

    Operating systems are complex, concurrent, and rapidly evolving software systems: the process model, hardware abstraction, storage and networking services, security primitives, and tracing/analysis/debugging tools are a critical foundation for our contemporary computing environments. This course teaches a blend of operating-system design and implementation as well as systems research methodology through a series of lectures and practical material split into three two-week sub-modules. The first module considers OS design, instrumentation, and measurement, motivated by a lab on I/O performance and the process model. The remaining two sub-modules focus on OS and micro-architectural analysis of Inter-Process Communication (IPC) and the Transport Control Protocol (TCP). Measurement and evaluation are a core aspect of the module: through labs and practical exercises, students will gain a deeper understanding of core principles in system behaviour, as well as analysis skills and intuitions that will serve them in future systems development and research. Lectures will consider methodology, systems principles, systems implementation, and current research in operating systems.

  • Affective Computing (L44) – Dr Hatice Gunes – 16 h

    NEW: Affective Computing is a multidisciplinary field of research and practice concerned with understanding, recognizing and utilizing human emotions, expressions and communicative behaviour in the design of computational systems ranging from user-adaptive entertainment technology (gaming/arts) to assistive technology in clinical and biomedical context (e.g., autism/depression) and designing social robots. Affective computing has established itself in the last 15 years as a cohesive sub-discipline in computer science with its own international conference (the International Conference on Affective Computing and Intelligent Interaction), journal (the IEEE Transaction on Affective Computing), and professional society (the Association for the Advancement of Affective Computing (AAAC, former HUMAINE Association). Progress in the field has triggered increasing industry interest – in January 2016 Apple Inc. purchased Emotient Inc., a startup that uses artificial-intelligence technology to read people’s emotions by analyzing facial expressions.

  • Algebraic Path Problems, with applications to Internet Routing (L11) – Dr Timothy Griffin – 16 h

    A great deal of interesting work was done in the 1970s in generalizing shortest path algorithms to a wide class of semirings – called “path algebras” or “dioids”. Although the evolution of Internet Routing protocols does not seem to have taken much inspiration from this work, recent “reverse engineering” efforts have demonstrated that an algebraic approach is very useful for both understanding existing protocols and for exploring the design space of future Internet routing protocols. This course is intended to present the basic mathematics needed to understand this approach. No previous background will be assumed. The course will start from scratch and end with open research problems. Many examples inspired by Internet Routing will be presented along the way.

  • Automated Reasoning (L18) – Dr Mateja Jamnik – 16 h

    Provides an introduction to how reasoning can be automated from an AI perspective. The course will introduce students to fundamental techniques for designing and implementing automated reasoners, and present advanced uses of theorem proving for solving mathematical problems via automated reasoning.

  • Category Theory and Logic (L108) – Prof Andrew Pitts – 16 h

    Category theory provides a unified treatment of mathematical properties and constructions that can be expressed in terms of "morphisms" between structures. It gives a precise framework for comparing one branch of mathematics (organized as a category) with another and for the transfer of problems in one area to another. Since its origins in the 1940s motivated by connections between algebra and geometry, category theory has been applied to diverse fields, including computer science, logic and linguistics. This course introduces the basic notions of category theory: adjunction, natural transformation, functor and category. We will use category theory to organize and develop the kinds of structure that arise in models and semantics for logics and programming languages.

  • Chip Multiprocessors (R05) – Dr Robert Mullins – 16 h

    This course provides an introduction to parallel computing with a particular focus on chip-multiprocessors. The course begins by examining the potential advantages of multi- and many-core processors. It explores the basics of parallel algorithm design, approaches to parallel programming and the architecture of modern chip-multiprocessors. The final seminar will be given by a guest speaker from industry.

  • Computer Security: Principles and Foundations (R209) – Dr Robert Watson, Prof Ross Anderson, Daniel Thomas – 16 h

    This course aims to provide students with an introduction to the history and central themes of computer security, from its 1970s foundations to some current research topics, with a theme of how to defend cloud-based systems against capable motivated opponents. The course considers first local computer systems and then distributed systems; however, we will rapidly discover that this is an artificial distinction that only becomes more awkward as we enter the current period. Throughout the course, we will consider proposed systems along with the adversarial research intended to identify gaps and vulnerabilities.

  • Computer Vision (E4F12) – Prof Roberto Cipolla, Dr Richard Turner, Prof Alan Blackwell, Dr Christopher Town, Dr Marwa Mahmoud – 16 h

    Lectures for this module are offered by the Department of Engineering. Assessment is carried out in the Computer Laboratory. The module aims to introduce the principles, models and applications of computer vision. The course will cover image structure, projection, stereo vision, and the interpretation of visual motion. It will be illustrated with case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces. Assessment will include practical exercises and a mini-project which in undertaken in Lent Term. There is no written examination for Part III or MPhil students.

  • Discourse Processing (R216) – Dr Simone Teufel – 16 h

    This module provides an introduction to NLP research centered around discourse processing (i.e., the means by which larger pieces of text are structured), and to text summarisation methods, particularly those based on discourse processing.

  • Introduction to Natural Language Syntax and Parsing (L95) – Prof Ted Briscoe – 16 h

    This module aims to provide a brief introduction to linguistics for computer scientists and then goes on to cover some of the core tasks in natural language processing (NLP), focussing on statistical tagging and parsing. We will look at how to evaluate taggers and parsers and see how well state-of-the-art tools perform given current techniques.

  • Introduction to networking and systems measurements (P50) – Dr Andrew Moore, Dr Noa Zilberman – 16 h

    NEW: This module will provide research skills for characterisation and modelling of systems and networks using measurements.

  • Machine Learning for Language Processing (L101) – Prof Ted Briscoe, Prof Ann Copestake – 16 h

    This module aims to provide an introduction to machine learning with specific application to tasks such as document classification, spam email filtering, language modelling, part-of-speech tagging, and named entity and event recognition for textual information extraction. We will cover supervised, weakly-supervised and unsupervised approaches using generative and discriminative classifiers based on graphical models, including (hidden) Markov models and CRFs, and clustering / dimensionality-reduction methods, such as latent Dirichlet allocation and neural word embeddings.

  • Modern Compiler Design (L25) – Dr David Chisnall, Dr Timothy Griffin – 16 h

    The LLVM project provides a modular set of libraries for building compilers, used in both academic research and industry. It therefore provides a rare opportunity for students to gain experience with a system that is both state-of-the-art in terms of research and also of direct industrial relevance. The course will focus largely one middle of the compiler – the analysis, transform, and optimisation pipeline – and will require the students to design and implement language-specific optimisations.

  • Multicore Semantics and Programming (R204) – Prof Peter Sewell, Dr Timothy Harris – 16 h

    In recent years multiprocessors have become ubiquitous, but building reliable concurrent systems with good performance remains very challenging. This module introduces some of the theory and the practice of concurrent programming, from hardware memory models and the design of high-level programming languages to the correctness and performance properties of concurrent algorithms.

  • Network Architectures (R02) – Prof Jon Crowcroft – 16 h

    The world needs more network architects! This module will discuss and critique historical and contemporary network architectures including ATM, TCP/IP and 3G, as well as cover emerging sensor networks and delay tolerant approaches.

  • Overview of Natural Language Processing (L90) – Dr Ekaterina Shutova, Dr Simone Teufel – 16 h

    This module introduces the fundamental techniques of natural language processing. It aims to explain the potential and the main limitations of these techniques. Some current research issues are introduced and some current and potential applications discussed and evaluated.

  • Principles of Data Science (L120) – Dr Richard Gibbens – 16 h

    This module will introduce students to the principles of Data Science that underpin key tools and techniques used both to describe and to gain insights into the properties of often large and complex datasets. The approach taken in the module will combine the development of mathematical theory with case studies taken from real-world application domains such as communications networks and road transport networks. The case studies will also highlight the use of modern software packages including R for both statistical computation as well as the graphical visualisation of statistical properties and results.

  • Probabilistic Machine Learning (E4F13) – Prof Carl Rasmussen – 16 h

    NB Module offered by the Department of Engineering. Probablistic machine learning is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world. The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

  • Research Skills Programme (RSP) – Dr Alastair Beresford, Prof Alan Blackwell, (and others) – 12 h

    To provide advice on and training in a variety of practical skills required for research. To provide training in a subset chosen from the diverse set of skills that will be useful in the other research-led modules, in the individual project, and in the student's future career. This programme must be taken by all M.Phil students and CPGS students.

  • Research Students Lectures (RSL) – Dr Andrew Rice – 6 h

    A series of lunchtime lectures prepared and given by Computer Laboratory research students.

  • Special topic MT (S500) – 16 h

    Up to one taught module may be replaced by a 'Special Topic'.

Lent term

  • A Mathematical Theory of Distributed Games and Strategies (L30) – Prof Glynn Winskel – 16 h

    NEW: This module will give an introduction to ongoing research in developing a mathematical theory of distributed games in which a Player (or a team of players) can interact and compete against an Opponent (or a team of opponents) in a highly distributed fashion, without, for instance, enforcing that their moves occur in a sequential fashion, or need to alternate. Although phrased in terms of `Player' and `Opponent' the dichotomy Player vs. Opponent can stand for Process vs. Environment, Prover vs. Disprover, or Ally vs. Enemy. These alternatives indicate the wide range of potential applications in computer science, logic, and beyond.

  • Advanced Functional Programming (L28) – Dr Jeremy Yallop, Dr Neel Krishnaswami – 16 h

    This module shows how to use the features of modern typed functional programming languages such as OCaml and Haskell to design and implement libraries and DSLs. It introduces a variety of programming techniques for improving both correctness and efficiency.

  • Advanced Operating Systems (L41)(continuing) – 16 h

    Operating systems are complex, concurrent, and rapidly evolving software systems: the process model, hardware abstraction, storage and networking services, security primitives, and tracing/analysis/debugging tools are a critical foundation for our contemporary computing environments. This course teaches a blend of operating-system design and implementation as well as systems research methodology through a series of lectures and practical material split into three two-week sub-modules. The first module considers OS design, instrumentation, and measurement, motivated by a lab on I/O performance and the process model. The remaining two sub-modules focus on OS and micro-architectural analysis of Inter-Process Communication (IPC) and the Transport Control Protocol (TCP). Measurement and evaluation are a core aspect of the module: through labs and practical exercises, students will gain a deeper understanding of core principles in system behaviour, as well as analysis skills and intuitions that will serve them in future systems development and research. Lectures will consider methodology, systems principles, systems implementation, and current research in operating systems.

  • Advanced Topics in Computer Systems (R01) – Dr Richard Mortier – 16 h

    An overview of “systems research”, a broad area covering operating systems, database systems, file systems, distributed systems and networking. The focus will be on critical thinking: the ability to argue for and/or against a particular approach or idea. Each week students will read and critique (and sometimes present) research papers in the field.

  • Advanced Topics in Natural Language Processing (R222) – Prof Ted Briscoe, Prof Ann Copestake, Dr Ronan Cummins, Dr Marek Rei, Dr Ekaterina Kochmar, Dr Mark Granroth-Wilding, Dr Tamara Polajnar, Dr Laura Rimell, Dr Helen Yannakoudakis – 16 h

    This module aims to cover at least four selected topics in enough detail and depth so that participants are potentially in a position to contribute to research on the topic. Each topic will be introduced with a lecture which, building on the material covered in the prerequisite courses, will make the current research literature accessible. The three seminar sessions will typically be run as a reading group with student presentations and discussion and will typically cover about six recent papers from the literature.

  • Advanced Topics in Semantics (M10) – Prof Marcelo Fiore – 8 h

    This module aims to bring students up to date with current research in the denotational approach to the semantics of programming languages and mathematical models of type theories, with the objective of training students to start research in theoretical computer science on these areas and their applications. Note: this module is not for credit.

  • Biomedical Information Processing (R214) – Dr Anna Korhonen, Dr Pietro Lio', Dr Nigel Collier – 16 h

    Research done within biomedical sciences is generating vast amounts of information which can, when analysed appropriately, improve our understanding of the complex processes that govern life, death and disease. This course surveys computational techniques that can be used to process biomedical data with the overall goal of supporting the processes of scientific inquiry, problem solving, and decision making in biomedical sciences. A variety of data types will be introduced, along with data and text mining techniques that can be used to analyse, extract, discover and integrate biomedical information at levels ranging from molecular through human populations. The course surveys specific problems in biology, clinical medicine and public health and shows how information processing can support practical applications in these areas.

  • Computer Security: Current Applications and Research (R210) – Prof Ross Anderson, Dr Robert Watson, Daniel Thomas – 16 h

    In the second security course in the ACS, we turn our attention to active research topics in computer security at the Computer Laboratory. One unifying theme is how to build secure systems at scale that contain more secure and less secure components. Building on the lessons from multilevel secure systems and security protocols discussed in the first course, we will explore infrastructure versus applications; services versus clients; the use of smartcards and other cryptographic processors; API security; and failure modes from covert channels to concurrency vulnerabilities.

  • Computer Vision (E4F12)(continuing) – 16 h

    Lectures for this module are offered by the Department of Engineering. Assessment is carried out in the Computer Laboratory. The module aims to introduce the principles, models and applications of computer vision. The course will cover image structure, projection, stereo vision, and the interpretation of visual motion. It will be illustrated with case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces. Assessment will include practical exercises and a mini-project which in undertaken in Lent Term. There is no written examination for Part III or MPhil students.

  • Data Centric Systems and Networking (R212) – Dr Eiko Yoneki – 16 h

    This module provides an introduction to data centric systems and networking, where data is a token in programming flow and networking and its impact on the computer system's architecture. Large-scale distributed applications with big data processing will grow ever more in importance and become a pervasive aspect of the lives of millions of users. Supporting the design and implementation of robust, secure, and heterogeneous large-scale distributed systems is essential.

  • High performance networking (P51) – Dr Andrew Moore, Dr Noa Zilberman – 16 h

    NEW: This module provides an introduction to High Performance Newtworking. It explores both software and hardware aspects and provides the students with an opportunity to experience high performance networking design and usage first hand.

  • Image Processing and Image Coding (E4F8) – Dr Joan Lasenby – 16 h

    NB: Module offered by the Department of Engineering. Sophisticated processing of images by digital hardware is now fairly common, and ranges from special effects in video games to satellite image enhancement. Three of the main application areas are video data compression, image enhancement, and scene understanding. This module introduces the key tools for performing these tasks, and shows how these tools can be applied. This module is offered by the Department of Engineering.

  • Interactive Formal Verification (L21) – Prof Larry Paulson – 16 h

    Introduces students to interactive theorem proving using Isabelle. It includes techniques for specifying formal models of software and hardware systems and for deriving properties of these models.

  • Machine Learning and Algorithms for Data Mining (L42) – Dr Mateja Jamnik, Dr Pietro Lio', Dr Thomas Sauerwald – 16 h

    This module aims to introduce students to basic principles and methods of machine learning algorithms that are typically used for mining large data sets. In particular, we will look into algorithms typically used for analysing networks, fundamental principles of techniques such as decision trees and support vector machines, and finally, neural network architectures. The students will gain practical understanding through a coding exercise where they will implement and apply one machine learning algorithm on a particular large data set.

  • Research Skills Programme (RSP)(continuing) – 12 h

    To provide advice on and training in a variety of practical skills required for research. To provide training in a subset chosen from the diverse set of skills that will be useful in the other research-led modules, in the individual project, and in the student's future career. This programme must be taken by all M.Phil students and CPGS students.

  • Social and Technological Network Data Analytics (L109) – Prof Cecilia Mascolo – 16 h

    This module aims to introduce concepts of complex and social network analysis and its application to real social and technological networks datasets. The course is complemented by practical analysis of large datasets of networks of various types using the concepts taught.

  • Special topic LT (S501) – 16 h

    Up to one taught module may be replaced by a 'Special Topic'.

  • System on Chip Design and Modelling (P35) – Dr David Greaves – 16 h

    A current-day system on a chip (SoC) consists of several different microprocessor subsystems together with memories and I/O interfaces. This practical module covers SoC design and modelling techniques with emphasis on architectural exploration, assertion-driven design and the concurrent development of hardware and embedded software.

Easter term

  • Critical Coding for Digital Humanities (M005) – Prof Alan Blackwell, Dr Anne Alexander – 12 h

    An immersive co-teaching course, in which students from different disciplinary backgrounds work in pairs to jointly create a novel software application. The course is primarily practical, with practical work carried out on the students' own laptops. It involves six two-hour sessions, taught over a single week (Friday to Thursday), and a presentation session on the final Friday.