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Department of Computer Science, Programme Catalogue 2018/19

TSCM-AWM48: MSc Machine Learning and Autonomous Systems

Leading to the award of MASTER OF SCIENCE IN MACHINE LEARNING AND AUTONOMOUS SYSTEMS

Mode of attendance: Full-time incorporating placement

Year 1

Follow this link for further information on the NFAAR NFAAR-PGT assessment regulations:
  • This programme has the following designated alternative programmes (DAPs):
    • TSCM-AFL48: PG Dip Machine Learning and Autonomous Systems
    • TSCM-AFC48: PG Cert Machine Learning and Autonomous Systems
  • Assessment Regulations: Appendix 11 PDF format -requires Acrobat Reader to view.
  • Programme progression requirement: 50%
  • Taught credits: 120; Dissertation/Project credits: 30


Semester 1: Taught Units
Compulsory Units:
Unit Code
Title
ECTS Credits
CM50264 Machine learning 1 6 credits       [equivalent to 12 CATS credits] Further information on credits
CM50272 Humans and intelligent machines 6 credits       [equivalent to 12 CATS credits] Further information on credits
XX50215 Statistics for data science 6 credits       [equivalent to 12 CATS credits] Further information on credits
EE50235 Autonomous systems engineering 6 credits       [equivalent to 12 CATS credits] Further information on credits

Optional Units: Select 1 unit from the following list:
Unit Code
Title
ECTS Credits
CM50206 Intelligent agents 6 credits       [equivalent to 12 CATS credits] Further information on credits
EE50237 Robotics software 6 credits       [equivalent to 12 CATS credits] Further information on credits


Semester 2: Taught Units
Compulsory Units:
Unit Code
Title
ECTS Credits
CM50175 Research project preparation 6 credits       [equivalent to 12 CATS credits] Further information on credits
CM50230 Intelligent control and cognitive systems 6 credits       [equivalent to 12 CATS credits] Further information on credits
CM50265 Machine learning 2 6 credits       [equivalent to 12 CATS credits] Further information on credits

Optional Units: Select 2 units from the following list:
Unit Code
Title
ECTS Credits
CM50263 Artificial intelligence 6 credits       [equivalent to 12 CATS credits] Further information on credits
CM50268 Bayesian machine learning 6 credits       [equivalent to 12 CATS credits] Further information on credits
CM50269 Neural computation 6 credits       [equivalent to 12 CATS credits] Further information on credits
CM50270 Reinforcement learning 6 credits       [equivalent to 12 CATS credits] Further information on credits


Dissertation Period: Taught Units
Compulsory Units:
This placement unit runs for 12 months from the end of Semester 2.
Unit Code
Title
ECTS Credits
CM50250 Placement 60 credits       [equivalent to 120 CATS credits] Further information on credits


Year 2

Follow this link for further information on the NFAAR NFAAR-PGT assessment regulations:
  • This programme has the following designated alternative programmes (DAPs):
    • TSCM-AFL48: PG Dip Machine Learning and Autonomous Systems
    • TSCM-AFC48: PG Cert Machine Learning and Autonomous Systems
  • Assessment Regulations: Appendix 11 PDF format -requires Acrobat Reader to view.
  • Programme progression requirement: 50%
  • Taught credits: 120; Dissertation/Project credits: 30


Dissertation Period: Project/Dissertation Units
Compulsory Units:
Unit Code
Title
ECTS Credits
CM50170 Dissertation 30 credits       [equivalent to 60 CATS credits] Further information on credits


Notes:

  • Programmes and units may be changed in accordance with normal University procedures.
  • This programme catalogue is applicable for all years of study for the 2018/19 academic year only. Students continuing their studies into 2019/20 and beyond should not assume that this programme, or its component units, will be delivered in future years in the format displayed here.
  • NFAAR assessment regulations are subject to review and update. Links given here will provide access to the latest versions of NFAAR documentation. The regulations described in this documentation may not necessarily be those which applied in previous academic years. For detailed information on how the NFAAR was applied to this programme in previous academic years, please contact the relevant Director of Studies.
  • Availability of units is subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.