Module Specification
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.
1. Module Title Neural Networks
2. Module Code ELEC320
3. Year Session 2023-24
4. Originating Department Electrical Engineering and Electronics
5. Faculty Fac of Science & Engineering
6. Semester Second Semester
7. CATS Level Level 6 FHEQ
8. CATS Value 7.5
9. Member of staff with responsibility for the module
Dr LJ Devlin Electrical Engineering and Electronics Lee.Devlin@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
Dr AF Garcia-Fernandez Electrical Engineering and Electronics Angel.Garcia-Fernandez@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 12

   6

       18
17.

Private Study

 57
18.

TOTAL HOURS

 75
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

 
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:
25. Programme(s) (including Year of Study) to which this module is available on a required basis:
26. Programme(s) (including Year of Study) to which this module is available on an optional basis:
27. 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 machine learning.
 
28. Learning Outcomes
 

(LO1) 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.
 

(LO2) 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.
 

(S1) On successful completion of this module the student should be able to pursue further study in artificial intelligence and more advanced types of neural networks.
 

(S2) On successful completion of this module the student should be able to analyse numerically the mathematical properties of most major network types and apply them to artificial intelligence problems.
 

(S3) On successful completion of this module the student should be able to approach methodically artificial intelligence problems and understand the principal mathematics of learning systems.
 
29. Teaching and Learning Strategies
 

Due to Covid-19, one or more of the following delivery methods will be implemented based on the current local conditions and the situation of registered students.

(a) Hybrid delivery, with social distancing on Campus

Teaching Method 1 - On-line asynchronous lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On average one per week

Teaching Method 2 - Synchronous face to face tutorials
Description: Tutorials on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On average one per fortnight

(b) Fully online delivery and assessment

Teaching Method 1 - On-line asynchronous lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On average one per week

Teaching Method 2 - On-line synchronous tutorials
Descrip tion: Tutorials on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On average one per fortnight

(c) Standard on-campus delivery with minimal social distancing

Teaching Method 1 - Lecture
Description: Lectures
Attendance Recorded: Yes

Teaching Method 2 - Tutorial
Description: Tutorials
Attendance Recorded: Yes
 
30. Syllabus
   

Introduction
Chapter 1 Structural Aspects
Chapter 2 Learning Processes
Chapter 3 Single-Layer Perceptron
Chapter 4 Multi-Layer Perceptron
Chapter 5 Radial-basis Function Networks
Chapter 6 Support Vector Machines
Chapter 7 Self-Organising Maps
 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment
32. EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
  Final Exam Assessment 1 Standard UoL penalty applies for late submission. Assessment Schedule (When) :Semester 2 examination period 0 100
33. CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes