Module Learning Outcomes Assessed:
1. Acquire a deep knowledge of the constitutional concepts of artificial neural networks including their
2. Apply and compare the different architectures and learning approaches available in neural network
3. Design and develop different neural network models applying appropriate learning approaches for
real world applications.
4. Use the available neural network simulators, develop solutions to real-world problems and appraise
5. Critically evaluate the trends in neural network developments.
Task and Mark distribution:
In this assignment, you will have to select a problem (e.g., a classification, prediction, modelling or
clustering problem), ideally inspired from the real world, and explore how best to apply neural network
learning algorithms to solve it. If you want to challenge yourself, you can choose either more recent
and advanced modelling approaches in neural networks (such as deep neural networks) and/or more
difficult applications, e.g., more complex problems from image processing, signal processing,
information retrieval, natural language processing, biology. The data set should be different from your
The main purpose of this assignment is to:
• Test the understanding on fundamental concepts of neural networks and their applications.
• Perform appropriate preparation of a data set and evaluate the performance of different neural
network algorithms on the chosen data set(s).
• Gain practical experience in using neural network learning algorithms for solving a real-life
• Demonstrate your ability to critically evaluate the results and compare different learning algorithms
and their results.
1. Please notify your registry course support team and module leader for disability support.
2. Any student requiring an extension or deferral should follow the university process as outlined here.
3. The University cannot take responsibility for any coursework lost or corrupted on disks, laptops or
personal computer. Students should therefore regularly back-up any work and are advised to save it on
the University system.
4. If there are technical or performance issues that prevent students submitting coursework through
the online coursework submission system on the day of a coursework deadline, an appropriate
extension to the coursework submission deadline will be agreed. This extension will normally be 24
hours or the next working day if the deadline falls on a Friday or over the weekend period. This will be
communicated via email and as Aula announcement.
5. You are encouraged to check the originality of your work by using the draft Turnitin links on your
Moodle Web. (A percentage of similarity higher than 30% is encouraged to be avoided.)
6. Collusion between students (where sections of your work are similar to the work submitted by other students
in this or previous module cohorts) is taken extremely seriously and will be reported to the academic
7. If you make use of the services of a proof reader in your work you must keep your original version
and make it available as a demonstration of your written efforts.
8. You must not submit work for assessment that you have already submitted (partially or in full), either
for your current course or for another qualification of this university, unless this is specifically provided
for in your assignment brief or specific course or module information. Where earlier work by you is
citable, ie. it has already been published/submitted, you must reference it clearly. Identical pieces of
work submitted concurrently will also be considered to be self-plagiarism.
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