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EE40098: Computational intelligence

[Page last updated: 15 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Electronic & Electrical Engineering
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 1
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Lab 1 (CW 20%)
  • Lab 2 (CW 20%)
  • Coursework (CW 60%)
Further information on supplementary assessment Supplementary Assessment:
EE40098 - Reassessment coursework (where allowed by programme regulations)
Further information on requisites Requisites:
Description: Aims:
To provide students with an understanding of some of the principles of Computational Intelligence.

Learning Outcomes:
After successfully completing this unit the student should be able to:
Understand, be able to explain and implement the following types of computational intelligence techniques:
* Optimization
* Control
* Machine Learning
* Classification
Understand and be able to explain the biological inspiration behind computational intelligence systems

Skills:
Application of the techniques introduced in the lectures to Computational Intelligence: taught, facilitated and tested.

Content:

* Optimization: simulated annealing, steepest descent, genetic algorithms, the schema Theorem, representation, populations, selection, crossover mutation , swarm optimization
* Control: Fuzzy Logic comparison with crisp logic. linguistic variables, degree of membership, fuzzy rules, defuzzification.
* Classification: Neural Networks, MCP neuron, geometric interpretation. XOR problem. 1, 2, and 3 layer feed-forward networks. perceptron training rule, sigmoid function, back-propagation, K-nearest neighbour, Principal Component Analysis, Convolutional Neural Networks Feed-back networks.
* Bio-inspired: Neurophysiology: biological neuron systems, neuron models for simulation, problem solving in nature, biomimetic computing, swarm Intelligence: emergent behaviour, predator-prey models, conways game of life
* Machine Learning: decision tree learning, rule based machine learning, deep learning, support vector machines, Bayesian networks, reinforcement learning.
Further information on programme availabilityProgramme availability:

EE40098 is Compulsory on the following programmes:

Department of Electronic & Electrical Engineering
  • TEXX-AFM01 : MSc Mechatronics
  • UEEE-AFM13 : MEng(Hons) Computer Systems Engineering (Year 4)
  • UEEE-AKM13 : MEng(Hons) Computer Systems Engineering with Year long work placement (Year 5)

EE40098 is Optional on the following programmes:

Department of Computer Science
  • USCM-AFM01 : MComp(Hons) Computer Science (Year 4)
  • USCM-AAM02 : MComp(Hons) Computer Science with Study year abroad (Year 5)
  • USCM-AKM02 : MComp(Hons) Computer Science with Year long work placement (Year 5)
Department of Electronic & Electrical Engineering
  • UEEE-AFM01 : MEng(Hons) Electrical and Electronic Engineering (Year 4)
  • UEEE-AKM01 : MEng(Hons) Electrical and Electronic Engineering with Year long work placement (Year 5)
  • UEEE-AFM12 : MEng(Hons) Electrical Power Engineering (Year 4)
  • UEEE-AKM12 : MEng(Hons) Electrical Power Engineering with Year long work placement (Year 5)
  • UEEE-AFM14 : MEng(Hons) Electronic Engineering with Space Science & Technology (Year 4)
  • UEEE-AKM14 : MEng(Hons) Electronic Engineering with Space Science & Technology with Year long work placement (Year 5)
  • UEEE-AFM15 : MEng(Hons) Electronic Systems Engineering (Year 4)
  • UEEE-AKM15 : MEng(Hons) Electronic Systems Engineering with Year long work placement (Year 5)
  • UEXX-AFM02 : MEng(Hons) Integrated Mechanical and Electrical Engineering (Year 4)
  • UEXX-AKM02 : MEng(Hons) Integrated Mechanical and Electrical Engineering with Year long work placement (Year 5)
Department of Mathematical Sciences

Notes: