Artificial Intelligence python code
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
IN-COURSE ASSESSMENT (ICA) SPECIFICATION
Ar#ficial Intelligence Founda#ons
CIS4049-N
In-course Assessment
Overview of Requirements
Assessment for Ar#ficial Intelligence Founda#ons (CIS4049-N) requires you to implement the Ar<ficial
Intelligence (AI) techniques to a case study of your choice and cri<cally evaluate the selec<on,
implementa<on, and experimental valida<on of the results.
The implemented AI solu<ons will be assessed by one in-course assessment consis<ng of:
• A wri5en report with a word limit of 4,000 words and an artefact or examples. Your report should
inves<gate and document the implementa<on of AI techniques to a real-world case study of
your choice, where you are required to cri<cally evaluate and reflect on the selec<on and the
applica<on of AI techniques, jus<fy the u<lisa<on of these techniques, and experimentally
validate the results. The artefact consists of either a single AI solu<on or a porMolio of work
(usually 2-3), demonstra<ng applica<on of the AI techniques to one or a small collec<on of
real-world case studies chosen by students (see sec<on ‘Requirements for the AI solu<on’ for
further details). Moreover, students are required to produce brief voice over walk-through
video (between 2 minutes and 5 minutes), showing and demonstra<ng what has been done
in the ICA. It is expected that the student will introduce his work and discuss what has been
achieved. It is also recommended that the student highlights the issues and limita<ons
encountered during implementa<on [100 points].
Further details are given below and there will be a suppor#ng briefing session on the ICA.
Submission of materials must be made via Backboard to the link provided. The submission date is
specified in the submission schedule.
Requirements for the AI solu5on
Your assessment requires you to produce a wri5en report and a walkthrough video:
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
AI solu#on (report equivalent to 4,000 words and walkthrough video) [100 points]
Design and implement AI techniques to a real-world case study of your choice. Provide a
reflec<on on your module experience and how you met the in-course assessment
requirements. Your report should document your learning and personal development,
providing evidence (e.g., screenshots or images of prac<cal or in-course assessment work)
where appropriate to support your solu<on. You should concentrate on what you learned
and how your knowledge and skill developed as you addressed the in-course assessment
and module content. Document the challenges you encountered and what you did to
resolve them. You could also consider how your experience may affect your future studies
and employment op<ons or choices. You could also design a personal development learning
plan based on your self-evalua<on. This should be in the form of a MS Word or PDF
document or an alterna<ve document in a readable format. The artefact consists of either
a single AI solu<on or a porMolio of work (usually 2-3), demonstra<ng applica<on of the AI
techniques to one or a small collec<on of real-world case studies chosen by students (see
sec<on ‘Requirements for the AI solu<on’ for further details). Moreover, you will also upload
the file containing all the source code of your solu<on (e.g., the .r and/or .py file(s)), and
please submit also the other files used for your experiments in a readable format.
The student is also required to produce a voice over brief walk-through video (2-3 minutes),
showing and demonstra<ng what has been done in the implemented AI solu<on. It is
expected that the student will introduce his work and discuss what has been achieved. It is
also recommended that the student highlights the issues and limita<ons encountered
during implementa<on.
This in-course assessment will meet all the learning outcomes: PTS1, PTS2, PTS3, RKC1,
RKC2, RKC3, PS1, PS2.
Learning Outcomes
Personal and Transferable Skills (PTS)
PTS 1. Effec<vely communicate and evaluate complex informa<on related to AI theory.
PTS 2. Use personal reflec<on to analyse self and own ac<ons in the course of working as a group or
individual in a real-world AI case study.
PTS 3. Reflect upon, take ownership of and cri<cally appraise the outcome of an implemented
solu<on against a given brief for a simulated or real-world problem using appropriate AI technologies.
Research, Knowledge and Cogni#ve skills (RKC):
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
RKC 1. Cri<cally analyse a solu<on to a given real world case study.
RKC 2. U<lise effec<vely AI soaware and techniques in problem solving.
RKC 3. Cri<cally appraise of recent scien<fic literature in AI in the context of a given scenario.
Professional skills (PS):
PS 1. Cri<cally evaluate the commercial risks and opportuni<es related to solving an AI problem.
PS 2. Autonomously evaluate improvements to performance drawing on innova<ve or best prac<ce in
applied AI related skills.
Outline Marking Scheme
Your submission will be assessed according to the following criteria:
1. Systema<c review of relevant scien<fic literature [40 points].
2. Cri<cal evalua<on and discussion of the significance of the applica<on of AI techniques [40
points].
3. Coverage of relevant commercial risks and professional issues [20 points].
Below is a provisional indica<on of the criteria applied to determine points for each element.
Please note:
Excep&onally, whilst points are allocated to specific parts, outstanding work in one area may be used to
trade-off points against poorer work in another area.
Review of
Scien#fic Literature
[40 points]
Concerns a cri&cal analysis of the scien&fic literature in the real-world
case addressed, and whether the implementa&on of the AI solu&on
derives from this thorough evalua&on
Excellent 70%
and above
The implemented AI solu<on derives from an extremely thorough
evalua<on and review of the scien<fic literature. The implemented
solu<on is very well connected with the related scien<fic literature. The
produced walk-through video demonstrates the excellent understanding
of the scien<fic literature, related to the chosen case study.
Very Good
60%-69%
The implemented AI solu<on correctly models the addressed real-world
case and meets all necessary requirements and performs consistently as
intended. Nevertheless, there is some missing references to the scien<fic
background. The produced walk-through video demonstrates a very good
understanding of the scien<fic literature, related to the chosen case
study.
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
Sa<sfactory
50-59%
Sa<sfactory implementa<on of the AI solu<on modeling the real-world
problem, even though the AI techniques adopted are not in line with the
scien<fic literature. Nevertheless, the implemented solu<on and code
meet a good propor<on of the requirements. The produced walkthrough
video demonstrates a sa<sfactory understanding of the scien<fic
literature, related to the chosen case study.
Fail
Less than 50%
Insufficient. The proposed solu<on is not supported by any scien<fic
literature. The produced walk-through video demonstrates a poor
understanding of the scien<fic literature, related to the chosen case study.
NS NON-SUBMISSION N/A
Evalua#on and
discussion of the
significance of the
applica#on of AI
techniques
[40 points]
Concerns whether the AI solu&on derives from a cri&cal evalua&on and
discussion of the AI techniques in a real-world case study, and also if the
implemented solu&on is significant.
Excellent 70%
and above
The proposed AI solu<on demonstrates a thorough evalua<on, discussion,
and correct applica<on of AI techniques in the real-world case study. The AI
solu<on operates without fatal error at run <me and fully sa<sfies the real-
world case requirements. The solu<on meets all necessary requirements
and performs consistently as intended. The code is clear with enough
comments. of the scien<fic literature, related to the chosen case study. The
produced walk-through video shows an excellent understanding of the
implemented AI solu<on.
Very Good
60%-69%
The proposed AI solu<on demonstrates a very good evalua<on, discussion,
and correct applica<on of AI techniques in the real-world case study. The
implemented AI solu<on operates at run-<me without fatal error with some
minor logic errors. The solu<on meets all necessary requirements and
performs consistently as intended. The code is not clear since there are not
enough comments. The produced walk-through video shows a very good
understanding of the implemented AI solu<on.
Sa<sfactory
50-59%
The proposed AI solu<on demonstrates a sa<sfactory evalua<on,
discussion, and applica<on of AI techniques in the real-world case study.
The implemented AI solu<on fails to operate or contains mistakes which
cause it to crash under certain condi<ons during run-<me but contains
evidence of ability to employ fundamental techniques to design an AI
solu<on. The produced walk-through video shows a sa<sfactory
understanding of the implemented AI solu<on.
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
Fail
Less than 50%
Insufficient. The proposed AI solu<on does not sa<sfy the minimum
requirements.
NS NON-SUBMISSION N/A
Coverage of relevant
commercial risks and
professional issues
[20 points]
Concerns the reflec&on on the relevant commercial risks and professional
issues of the implemented AI solu&on.
Excellent 70%
and above
There is extensive commentary on the relevant commercial risks and
professional issues associated with the implemented AI solu<on,
demonstrated by a cri<cal and elaborated discussion. The produced
walkthrough video shows an extensive and cri<cal reflec<on on relevant
commercial risks and professional issues.
Very Good
60%-69%
There is substan<al commentary on the relevant commercial risks and
professional issues of the implemented AI solu<on demonstrated by a
consistent discussion. The produced walk-through video shows a
substan<al reflec<on on relevant commercial risks and professional
issues.
Sa<sfactory
50-59%
There is some useful commentary about the relevant commercial risks and
professional issues of the implemented AI solu<on and the whole learning
experience of the ICA and studying the module. Nevertheless, it tends to
be descrip<ve rather than reflec<ve and needs to concentrate more on
personal evalua<on and development. The submieed report would
benefit from including more suppor<ve evidence (e.g., schema<cs,
screenshots, research, etc.). It could also benefit from iden<fying more
learning needs and how they might be addressed. The produced
walkthrough video shows a sa<sfactory reflec<on on relevant commercial
risks and professional issues.
Fail
Less than 50%
Insufficient. Any reflec<on of relevant commercial risks and professional
issues of the implemented AI solu<on.
NS NON-
SUBMISSION
N/A
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
Deliverables & Submission
You are required to submit your work to Blackboard via the assessments link by the due date.
Regarding the submission of your AI solu<on, you will upload a report (equivalent to 4,000 words)
that should document the AI solu<on and your personal reflec<ons. Also, in this case you are free to
choose the <tle of your report, but do not forget to always include your student ID, name, and
surname: “studentID_lastname_firstname_<tle_of_your_report.docx”.
Moreover, you will also upload the R or Python file containing all the source code of your
solu<on (e.g., the .r and/or .py file(s)). Please label this file with your student ID, name and
surname: ” studentID_lastname_firstname.r (or .py)” or, alterna<vely, if you prefer, you can
include also the name of your solu<on, so: “studentID_lastname_firstname_name_of
your_solu<on.r (or .py)”. Please submit also the other files used for your experiments in a
readable format.
Moreover, you will upload a voice over brief walk-through video (2-3 minutes), showing and
demonstra<ng what has been done in the dis<nct parts of the project. It is expected that the
student will introduce his work and discuss what has been achieved. It is also recommended
that the student highlights the issues and limita<ons encountered during implementa<on.
You may use a zip file to package your submission artefacts (i.e., the zip file containing your
report, the source code and all the other files used for your experiments, and the walkthrough
video).
All the submieed files within the zip file should be labelled as follows for iden<fica<on purposes:
studentID_lastname_firstname.zip (e.g. x1234567_smith_jane.zip)
Your report, the source code of the implemented AI solu<on and all the other files used for
your experiments, and the walk-through video should also be labelled in a similar manner
with your student ID.
Make sure your student ID and name is present on all documenta#on you submit.
Logis6cs
Aaer the ICA briefing has been given, you will be provided with opportuni<es to progress your
in-course work during some <metabled sessions. Feedback – but not points – will be given on
your work in progress to assist you in submijng a considered and well-developed ICA
submission.
ICA-SPECIFICATIONS-CIS4049-N-2023-2024
Academic Misconduct and Plagiarism
Please note that the University takes the issue of academic misconduct and plagiarism
very seriously. You should not copy anyone else’s work or use copyright materials
without due acknowledgement.
