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CM50269: Neural computation

Academic Year: 2018/9
Owning Department/School: Department of Computer Science
Credits: 6      [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CW 100%
Assessment Detail:
  • Written Report on Case Study (CW 50%)
  • Completed Lab Report (CW 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Description: Aims:
To convey the detailed theory and practice of a wide range of contemporary and historical neural computation (and related) data modelling approaches.

Learning Outcomes:
After completion of the unit, students should be able to:
* critically appraise the historical development of the field,
* advocate the relevance of neural computation models to problems in data science,
* explain in detail the theory behind key neural computing models,
* implement a neural network model from first principles in a relevant programming language (e.g. Python),
* apply complex neural computing model software (e.g. for "deep learning") in a data science context and critically evaluate the results.

Skills:
Intellectual skills:
* Conceptual understanding of modelling architectures (T,F,A)
* Critical analysis of algorithms (T,F,A)
Practical skills:
* Implementation of neural computing algorithms (T,F,A)
* Application of deep learning models (T,F,A)
Transferable skills:
* Numerical programming (F,A)
* Optimisation methods (F,A)

Content:
Topics covered include the history of the field and development of artificial neural network models, the variety of neural computing paradigms, technical aspects arising in the fitting of models (e.g. nonlinear optimisation), and motivation for and use of contemporary "deep learning" approaches.
Programme availability:

CM50269 is Optional on the following programmes:

Department of Computer Science

Notes:

  • This unit catalogue is applicable for the 2018/19 academic year only. Students continuing their studies into 2019/20 and beyond should not assume that this unit will be available in future years in the format displayed here for 2018/19.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be 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.
  • Undergraduates: .
  • Postgraduates: .