Module Details
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Computational Intelligence
Code COMP575
Coordinator Dr JYI Goulermas
Computer Science
J.Y.Goulermas@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2018-19 Level 7 FHEQ Second Semester 15

Aims

Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks.

Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised mapping.

Understand the benefit of adopting naturally inspired techniques to implement optimisation of complex systems and acquire the fundamental knowledge in various evolutionary techniques.

Bec ome familiar with the basic concepts of systems optimisation and its role in natural and biological systems and entities.

Learning Outcomes

Learning  the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms.

Appreciation of the advantages of evolutionary-related approaches for optimisation problems and their advantages compared to traditional methodologies. Also, understanding the different techniques of evolutionary optimisation for discrete and continuous configurations

Understanding of the needs for genetic encoding and modelling for solving optimisation problems and familiarisation with the evolutionary operators and their performance.

Understanding of the neural network learning processes and their most popular types, as well as  appreciation of how neural networks can be applied to artificial intelligence problems.

Syllabus
1 

For Neural Networks (part I), 12 lectures delivering the following chapters:

  • Introduction: Chapter 1
  • Structural Aspects: Chapter 2
  • Learning Processes: Chapter 3
  • Single-Layer Perceptrons: Chapter 4
  • Multi-Layer Perceptrons: Chapter 5
  • Radial-basis Function Networks: Chapter 6
  • Support Vector Machines: Chapter 7
  • Self-Organising Maps: Chapter 8

For Evolutionary Computation (part II), 12 lectures delivering the following chapters:
  • Introduction: Chapter 1
  • Genetic Algori thms: Chapter 2 (basic elements), Chapter 3 (advanced topics), Chapter 4 (theoretical analysis)
  • Genetic Programming: Chapter 5 (genetic programming & gene expression programming)
  • Particle Swarm Optimisation: Chapter 6 (overview and extensions)
  • Evolutionary Strategies: Chapter 7 (overview and extensions)

Teaching and Learning Strategies

Lecture - Part 1: Neural Networks (12) and Part 2: Evolutionary Computation (12)

Tutorial - Slide Presentation and Blackboard

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24
Part 1: Neural Networks (12) and Part 2: Evolutionary Computation (12)
  12
Slide Presentation and Blackboard
      36
Timetable (if known)              
Private Study 114
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Unseen Written Exam  180  Semester 2 Exam period  100  Yes  Standard UoL penalty applies  Exam Notes (applying to all assessments) - none 
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
Explanation of Reading List: