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. |
Title | NEURAL NETWORKS | ||
Code | ELEC320 | ||
Coordinator |
Dr AF Garcia-Fernandez Electrical Engineering and Electronics Angel.Garcia-Fernandez@liverpool.ac.uk |
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Year | CATS Level | Semester | CATS Value |
Session 2020-21 | Level 6 FHEQ | Second Semester | 7.5 |
Aims |
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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. |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
Co-requisite modules: |
Learning Outcomes |
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(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. |
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(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. |
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(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. |
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(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. |
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(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. |
Syllabus |
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Introduction |
Teaching and Learning Strategies |
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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 Teaching Method 2 - Synchronous face to face tutorials (b) Fully online delivery and assessment Teaching Method 1 - On-line asynchronous lectures Teaching Method 2 - On-line synchronous tutorials (c) Standard on-campus delivery with minimal social distancing Teaching Method 1 - Lecture Teaching Method 2 - Tutorial |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
12 |
6 |
18 | ||||
Timetable (if known) | |||||||
Private Study | 57 | ||||||
TOTAL HOURS | 75 |
Assessment |
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EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Assessment 1 Standard UoL penalty applies for late submission. Assessment Schedule (When) :Semester 2 examination period | 2 hours | 70 | ||||
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Due approximately week 9 | 30 |
Reading List |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |