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Department of Computer Science and Technology

Courses 2020–21

 

Course pages 2020–21

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'.

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) – Dr Robert Mullins, Prof Simon Moore, Dr Timothy Jones – 16 h

    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, behaviour monitoring, cyber-physical systems, autonomous vehicles, drones. The course will cover current and seminal research papers in the area of research. In 2020-21, this is a single module that starts in November, Michaelmas term, and carries over into, and is assessed in, 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.

  • Computer Security: Principles and Foundations (R209) – Prof Ross Anderson, Prof Alastair Beresford, Dr Robert Watson, Dr Alice Hutchings – 16 h

    This course aims to give students 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.

  • Data Science: principles and practice (L330) – Dr Ekaterina Kochmar, Prof Ted Briscoe – 16 h
  • 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.

  • 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 Ted Briscoe, 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.

  • 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. Integrating machine learning approaches (e.g. Bayesian Optimisation, Reinforcement Learning) for system optimisation will be also explored in this course.

  • Machine Learning and the Physical World (L48) – Prof Neil Lawrence, Dr Carl Henrik Ek – 16 h

    The module “Machine Learning and the Physical World” is focused on machine learning systems that interact directly with the real world. Building artificial systems that interact with the physical world have significantly different challenges compared to the purely digital domain. In the real world data is scares, often uncertain and decisions can have costly and irreversible consequences. However, we also have the benefit of centuries of scientific knowledge that we can draw from. This module will provide the methodological background to machine learning applied in this scenario. We will study how we can build models with a principled treatment of uncertainty, allowing us to leverage prior knowledge and provide decisions that can be interrogated.

  • Machine Learning for Language Processing (L101) – Dr Andreas Vlachos, Prof Ted Briscoe – 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.

  • 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) – Dr Weiwei Sun, Dr Andrew Caines, Prof Paula Buttery – 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. Students will also be introduced to practical experimentation in natural language processing.

  • Principles of Machine Learning Systems (L46) – Dr Nic Lane – 16 h

    This course will examine the emerging principles and methodologies that underpin scalable and efficient machine learning systems. Primarily, the course will focus on an exciting cross-section of algorithms and system techniques that are used to support the training and inference of machine learning models under a spectrum of computing systems that range from constrained embedded systems up to large-scale distributed systems. It will also touch up the new engineering practices that are developing in support of such systems at scale. When needed to appreciate issues of scalability and efficiency, the course will drill down to certain aspects of computer architecture, systems software and distributed systems and explore how these interact with the usage and deployment of state-of-the-art machine learning.

  • Probabilistic Machine Learning (LE49) – Dr Damon Wischik, Prof Carl Rasmussen, Dr Carl Henrik Ek – 17 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. 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 be required to take a minimum number of units of varying length logged across three terms.

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

    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.

Lent 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.

  • Advanced Topics in Computer Systems (R01) – Dr Heidi Howard, Prof Jon Crowcroft – 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.

  • Advanced topics in machine learning or natural language processing (R250) – Dr Andreas Vlachos, Prof Mateja Jamnik – 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, behaviour monitoring, cyber-physical systems, autonomous vehicles, drones. The course will cover current and seminal research papers in the area of research. In 2020-21, this is a single module that starts in November, Michaelmas term, and carries over into, and is assessed in, 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.

  • Computer Vision (L248) – Dr Christopher Town, Prof John Daugman, 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 aims of this course are to introduce the principles, models and applications of computer vision, as well as some mechanisms used in biological visual systems that may inspire design of artificial ones. The course will cover: image formation, structure, and coding; edge and feature detection; neural operators for image analysis; texture, colour, stereo, and motion; wavelet methods for visual coding and analysis; interpretation of surfaces, solids, and shapes; probabilistic classifiers; visual inference, recognition, and learning.

  • Cybercrime (R254) – Dr Alice Hutchings, Prof Ross Anderson, 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.

  • Distributed Ledger Technologies: Foundations and Applications (L47) – Prof Srinivasan Keshav – 16 h

    This reading group course examines foundations and current research into distributed ledger (blockchain) technologies and their applications. Students will read, review, and present seminal research papers in this area. Once completed, students should be able to integrate blockchain technologies into their own research and gain familiarity with a range of research skills.

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

    This course provides a practical introduction to hardware security, with a focus on techniques for reverse engineering digital devices. Students will complete 4–6 practicals centred around microcontroller development, printed-circuit board reverse engineering, firmware extraction and analysis, decompilation, protocol analysis and elliptic-curve cryptography. 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.

  • Interaction with Machine Learning (P230) – Prof Alan Blackwell, Dr Advait Sarkar – 16 h

    This is an advanced course in human-computer interaction, with a specialist focus on intelligent user interfaces and interaction with machine-learning and artificial intelligence technologies. The format will be largely practical, with students carrying out an original empirical research investigation over the course of one term. All empirical studies will address human interaction with some kind of model-based system for planning, decision, automation etc. Possible study formats might include: System evaluation, Field observation, Hypothesis testing experiment, Design intervention, Corpus analysis, or others as shown to be appropriate from evidence of prior research publications that have adopted specific empirical formats.

  • 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.

  • 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 be required to take a minimum number of units of varying length logged across three terms.