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Department of Computer Science and Technology

Courses 2022–23

 

Course pages 2022–23 (working draft)

Part II CST

In addition to courses examined in Tripos papers 8 and 9, students also select two further units of assessment, which are examined separately.

Please check the official timetables for timetable details.

Michaelmas term

Units of assessment:

Lent term

Units of assessment:

  • Advanced Operating SystemsDr Robert Watson – 16 h
  • Advanced RoboticsDr Amanda Prorok, Dr Fulvio Forni, Dr Fumiya Iida, Dr Hatice Gunes – 16 h

    This module aims to extend the knowledge and skills of students in designing and developing autonomous machines and researching robotics-related topics. Beyond the Introduction to Robotics course given in Michaelmas Term, the Advanced Robotics course will focus on more advanced topics such as Robot Learning, Underactuated Robot Control, Soft Robotics, Human-Robot Interaction, and Multi-Agent Systems.

  • Computer Systems ModellingProf Srinivasan Keshav – 16 h

    The aims of this course are to introduce the concepts and principles of mathematical modelling and simulation, with particular emphasis on using queuing theory and control theory for understanding the behaviour of computer and communications systems.

  • CybercrimeDr Alice Hutchings, Prof Ross Anderson, Dr Richard Clayton – 16 h
  • Deep Neural NetworksDr Ferenc Huszar, Dr Nic Lane – 14 h

    The module “Deep Learning and Neural Networks” is focussed on teaching the fundamental ideas behind deep neural network solutions that are being widely deployed in domains such as computer vision, speech recognition and language technology.

  • Extended Reality – 16 h
  • Federated Learning – 12 h

    This course aims to extend the machine learning knowledge available to students in Part I, and allow them to understand how these concepts can manifest in a decentralized setting. The course will consider both theoretical (e.g., decentralised optimisation) and practical (e.g. networking efficiency) aspects that combine to define this growing area of machine learning. At the end of the course students should: • Understand popular methods used in federated learning • Be able to construct and scale a simple federated system • Have gained an appreciation of the core limitations to existing methods, and the approaches available to cope with these issues • Developed an intuition for related technologies like differential privacy and secure aggregation, and are able to use them within typical federated settings • Can reason about the privacy and security issues with federated systems

  • Mobile HealthProf Cecilia Mascolo – 16 h

Easter term