Department of Computer Science and Technology

Course pages 2019–20

MPhil ACS

Students select five taught modules in total, distributed across Michaelmas and Lent terms. Students should select no more than four modules from those offered in Michaelmas and no more than two modules from those offered in Lent. A module that starts in Michaelmas term and continues in Lent term will count as a single module. The taught modules are delivered in a range of styles and have a variety of assessment methods. For example: traditional Lecture-based modules which have the prefix 'L' such as 'L11 Algebraic path problems'; modules with a large proportion of Practical classes have the prefix 'P' such as 'P51 High performance networking'; Reading clubs or seminar style modules have the prefix 'R' such as 'R254 Cybercrime'; and for modules where Lectures are borrowed from the Department of Engineering, we use the prefix 'LE', such as 'LE49 Probabilistic machine learning'. NB: These pages are still under construction.

Please check the official timetables for timetable details.

Michaelmas term

  • Advanced Graphics and Image Processing (L352) – Dr Rafal Mantiuk – 16 h

    Advanced Graphics covers topics related to processing, perception and display of images. The focus of the course is on the algorithms behind new emerging display technologies, such as virtual reality, augmented reality, and high dynamic range displays. It complements two computer graphics courses, Introduction to Graphics and Further Graphics, by introducing problems that became the part of graphics pipeline: tone-mapping, post-processing, displays and models of visual perception.

  • Advanced Topics in Computer Architecture (R265) – Prof Simon Moore, Dr Robert Mullins, Dr Timothy Jones – 16 h

    New: This course aims to provide students with an introduction to a range of advanced topics in computer architecture. It will explore the current and future challenges facing the architects of modern computers. These will also be used to illustrate the many different influences and trade-offs involved in computer architecture.

  • Advanced Topics in Mobile Systems and Mobile Data Machine Learning (R249) – Prof Cecilia Mascolo, Dr Jagmohan Chauhan – 16 h

    This module aims to introduce the latest research advancements in mobile systems and mobile data machine learning, spanning a range of domains including systems, data gathering, analytics and machine learning, on device machine learning and applications such as health, transportation, behavior monitoring, cyber-physical systems, autonomous vehicles, drones. The course will cover current and seminal research papers in the area of research. In 2019-20, this is a single module that starts in November, Michaelmas term, and carries over into Lent term. It is not a double module.

  • Algebraic Path Problems (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) – Prof 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 (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.

  • Cybercrime (R254) – Dr Alice Hutchings, Dr Richard Clayton – 16 h

    This module examines major topics relating to cybercrime from an interdisciplinary perspective. These include offense types and techniques, targets, victimisation, social and financial cost, criminal marketplaces, offenders, detection and prevention, and regulation and policing. The module outlines: Key debates in cybercrime research; how crime is committed using computer systems; and provides an understanding of how cybercrime is regulated, policed, detected and prevented.

  • Digital Signal Processing (L314) – Dr Markus Kuhn – 16 h

    This course teaches the basic signal-processing principles necessary to understand many modern high-tech systems, with application examples focussing on audio processing, audio and image coding, communication systems, software-defined radio, and linear feed-back control. Students will gain practical experience from numerical experiments in programming assignments.

  • Hardware Security (P232) – Dr Markus Kuhn, Dr Sergei Skorobogatov, Dr Franck Courbon, Shih-Chun You – 16 h

    NEW: This course provides a practical introduction to hardware security, with a focus on techniques for reverse engineering digital devices. Students will complete four practicals centred around printed-circuit board reverse engineering, firmware extraction and analysis, side-channel attacks on cryptographic algorithms implemented in microcontrollers, and silicon-level data extraction. Practicals will be followed by reading groups to discuss state-of-the-art techniques. At the end, students should have gained some hands-on experience in reverse-engineering a product, better understand the problem of hardening a product design against reverse engineering and tampering, and be familiar with a range of hardware-level attack techniques and countermeasures.

  • 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.

  • Introduction to Natural Language Syntax and Parsing (L95) – Prof Simone Teufel, Prof Paula Buttery – 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 (L50) – Prof Andrew Moore, Dr Noa Zilberman – 16 h

    Systems research refers to the study of a broad range of behaviours arising from a complex system design, including: resource sharing and scheduling; interactions between hardware and software; network topology, protocol and device design and implementation; low-level operating systems; Interconnect, storage and more. This module will: teach performance measurement methodology and practice through profiling experiments; expose students to real-world systems artefacts evident through different measurement tools; develop scientific writing skills thorough a series of laboratory reports; and provide research skills for characterization and modelling of systems and networks using measurements.

  • Large-scale data processing and optimisation (R244) – Dr Eiko Yoneki – 16 h

    This module provides an introduction to large-scale data processing, optimisation, and the impact on computer system's architecture. Large-scale distributed applications with high volume data processing such as training of machine learning will grow ever more in importance. Supporting the design and implementation of robust, secure, and heterogeneous large-scale distributed systems is essential. To deal with distributed systems with a large and complex parameter space, tuning and optimising computer systems is becoming an important and complex task, which also deals with the characteristics of input data and algorithms used in the applications. Algorithm designers are often unaware of the constraints imposed by systems and the best way to consider these when designing algorithms with massive volume of data. On the other hand, computer systems often miss advances in algorithm design that can be used to cut down processing time and scale up systems in terms of the size of the problem they can address. Integrating machine learning approaches for system optimisation will also be explored in this course.

  • Machine Learning for Language Processing (L101) – Dr Andreas Vlachos, Dr Ryan Cotterell – 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 network architectures.

  • Multicore Semantics and Programming (L304) – 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) – Prof Paula Buttery, Dr Ryan Cotterell – 18 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.

  • Probabilistic Machine Learning (LE49) – Prof Carl Rasmussen, Dr Damon Wischik – 16 h

    Probabilistic 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. In 2019-20, this is a single module that starts in Michaelmas Term and carries over into Lent Term. It is not a double module.

  • Research Skills Programme (RSP) – 24 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. Note that this is not one of the five modules taken for the examination of the M.Phil and should not be chosen as a preferred module. Students will take a minimum of 12 units of varying length logged across three terms.

  • Special topic MT (S500) – 16 h

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

  • Technology, law and society (R260) – Dr Jatinder Singh, Dr Jennifer Cobbe, Dr Heleen Janssen, Dr Ian Wassell – 16 h

    NEW: Data-driven technologies are increasingly the subject of social commentary, political scrutiny and regulatory attention. This module aims to develop a solid understanding of the practical implications these concerns have on systems design and engineering. Areas explored include the legal foundations in data protection (GDPR), privacy, liability, human rights; issues of tech-surveillance and algorithmic accountability; and the related implications for technologies including cloud, machine learning and the IoT. This course provides students with a practical background regarding how law, policy and societal concerns interact with technology. This is to develop an awareness and consideration of how systems can be designed and engineered to be more accountable, legally compliant, and generally better for society.

Not-for-credit modules:

  • Data Science: principles and practice (M20) – Dr Ekaterina Kochmar, Dr Guy Emerson, Dr Damon Wischik – 12 h

    The course will develop core areas of Data Science (eg models for regression and classification) from several perspectives: conceptual formulation and properties, solution algorithms and their implementation, data visualization for exploratory data analysis and the effective presentation of modelling outputs. The lectures will be complemented by practical classes using Jupyter notebooks and relevant software such as Python, R and Spark. The course may be suitable for MPhil students who have not studied statistical learning (machine learning) at an undergraduate level. It is not offered for examination and cannot be chosen as one of an MPhil student's five modules.

Lent term

  • Advanced Operating Systems (L41) – Dr Graeme Jenkinson, 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.

  • Advanced Topics in Computer Systems (R01) – Dr Richard Mortier, Dr Heidi Howard – 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 machine learning and natural language processing (R250) – Prof Simone Teufel, Prof Mateja Jamnik, Dr Ryan Cotterell, Dr Andreas Vlachos – 16 h

    This course explores current research topics in machine learning and/or their application to natural language processing in sufficient depth that, at the end of the course, participants will be in a position to contribute to research on their chosen topics. 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. Each lecture will be followed by up to three seminar sessions which will typically be run as a reading group with student presentations on recent papers from the literature followed by a discussion.

  • Advanced Topics in Mobile Systems and Mobile Data Machine Learning (R249)(continuing) – 16 h

    This module aims to introduce the latest research advancements in mobile systems and mobile data machine learning, spanning a range of domains including systems, data gathering, analytics and machine learning, on device machine learning and applications such as health, transportation, behavior monitoring, cyber-physical systems, autonomous vehicles, drones. The course will cover current and seminal research papers in the area of research. In 2019-20, this is a single module that starts in November, Michaelmas term, and carries over into Lent term. It is not a double module.

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

    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.

  • Computer Security: Principles and Foundations (R209) – Prof Ross Anderson, Prof Alastair Beresford, Dr Robert Watson, Dr Alice Hutchings – 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 (L248) – Prof John Daugman, Dr Christopher Town, Dr Marwa Mahmoud – 18 h

    Lectures for this module are borrowed from the undergraduate CST Part II Computer Vision. MPhil students undertake practical exercises and a mini-project in Lent Term. 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. There is no written examination for MPhil students. It is not a double module.

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

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

  • Machine learning for programming (P252) – Dr Andrew Rice, Miltos Allamanis, Marc Brockschmidt – 16 h

    This module investigates how machine learning techniques can be applied to the analysis and synthesis of computer programs. A variety of machine learning techniques will be covered considering different applications such as program repair, code suggestion, summarisation and comment-generation. The course is based around the implementation of machine learning tasks. For example, ‘CommentGeneration: generate a Javadoc comment for a method in a class’ or ‘VarNaming: given a piece of source code with the name of a single variable removed predict the missing name’. Module attendees will be given a list of tasks and their descriptions before the course starts and asked to rank them in order of preference. Each student will be assigned a task to implement over the course. We will suggest relevant papers to form the starting point for each project.

  • Mobile Robot Systems (L310) – Dr Amanda Prorok – 16 h

    This course teaches the foundations of autonomous mobile robots, covering topics such as perception, motion control, and planning. It also teaches algorithmic strategies that enable the coordination of multi-robot systems and robot swarms. The course will feature several practical sessions with hands-on robot programming. The students will undertake mini-projects, which will be formally evaluated through a report and presentation.

  • Probabilistic Machine Learning (LE49)(continuing) – 16 h

    Probabilistic 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. In 2019-20, this is a single module that starts in Michaelmas Term and carries over into Lent Term. It is not a double module.

  • Research Skills Programme (RSP)(continuing) – 24 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. Note that this is not one of the five modules taken for the examination of the M.Phil and should not be chosen as a preferred module. Students will take a minimum of 12 units of varying length logged across three terms.

  • Special topic LT (S501) – 16 h

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

  • Topics in Concurrency (L301) – Prof Glynn Winskel – 16 h

    The aim of this course is to introduce fundamental concepts and techniques in the theory of concurrent processes. It will provide languages, models, logics and methods to formalise and reason about concurrent systems. The course is borrowed from Part II of the Computer Science Tripos but is assessed differently.

  • Topics in Logic and Complexity (L15) – Prof Anuj Dawar – 16 h

    The course covers the use of methods from logic in the study of computational complexity as well as aspects of logic that can be understood from the perspective of complexity. In particular, it focuses on the study of the expressive power of logical formalisms and methods for proving limitations on this.