Artificial Intelligence – Project Design & Evaluation

Page 1 of 5

ASSESSMENT TASK

This assignment addresses all four Intended Learning Outcomes (ILOs) for this unit (see below).

There are two parts to the submission (more on this below).

1. The source code (to solve a chosen problem) that you have implemented, to provide evidence of

independent, technical, work (35% of total mark).

2. A technical report that covers the description of the problem, the methodology, and an empirical

investigation (40% of total mark).

A key aspect of this assessment is demonstrating the ability to perform a critical analysis and evaluation.

This involves empirical experiments, evaluating the performance of artificial intelligence algorithms and,

potentially, data processing techniques, depending on the problem at hand.

You can choose to implement one of the algorithms we cover in the class or other algorithms as required

by the targeted problem. In all cases, full details must be provided both in the documentation of the code

and the report. The implementation must be in Python, Java or Matlab.

You are given the opportunity to choose yourself:

1) The project you are interested in any AI applications: natural language processing and

understanding, machine vision, speech recognition, robotics, intelligent agents, smart

environments, etc.

2) Your teammates (3 people max) – please give a name to your team. While individual projects are

possible, you are encouraged to join a team. Projects developed by one person will be evaluated

on that basis, but still according to the same assessment criteria.

You are asked to propose a project idea by following the traditional workflow:

- What is the problem to be solved?

- Why are you interested in this particular problem?

- Does the problem need datasets to be available? If so, which dataset is to be used?

- Which approach is appropriate for solving that problem? Please describe exactly the steps i.e. how

you are going to deal with the problem at hand.

- Which algorithms are planned for the application?

- Which quality measures are to be used to evaluate the algorithms?

Faculty of Science and Technology - Department of Computing & Informatics

Unit Title: Machine Learning

Assessment Title: Artificial Intelligence – Project Design & Evaluation

Unit Level: 7 Assessment Number: 1 of 1

Credit Value of Unit: 20 

Marker(s): Hamid Bouchachia 

Quality Assessor: Marcin Budka Submission Location: Turnitin (large file submission)

Feedback method: Brightspace

This is a group assignment [max 3 students per group] which carries 100% of the final unit mark

split as follows:

1- 40% Report

2- 35% Code

3- 25% Presentation (this is not to part of the submission)

June 2019 v1

Page 2 of 5

If you find it challenging to come up with your own project idea, you will need to discuss with the Unit Leader

(UL) for advice and potential ideas by arranging such a discussion any date before 30/03/2020. In all

cases, please make sure to submit your proposal (a brief description that covers the questions mentioned

earlier) as soon as you have made your choice, but the deadline for all submissions is 30/03/2020 (the

latest). Please note that you can submit your proposal any time before this date from now on, so

that you have more time to develop your project.

Please note that for your guidance, a sample of datasets will be made available on Brightspace (under the

“Assessment” option). To learn about these datasets, please read the corresponding documentation

(potentially the “Readme.txt” file once you have downloaded it). You can use these datasets or propose

others, depending on the idea you are exploring in your project.

Deliverables

1. D1: The proposal to be submitted that includes a brief description that covers the questions

mentioned earlier. Deadline is 30/03/2020. Please name your proposal using the name of your

team “TeamName_AI.doc” (or .pdf) and submit through Turnitin (first box).

2. D2: A detailed report that contains the following sections: Front matter, Problem definition,

Methodology (all steps), Experiments & discussion, Conclusion and references. Please name it

Report_AI.doc (or .pdf).

3. D3: Working and well-documented code. Please zip it and name it Code_AI.zip or Code_AI.rar.

If you are using tools to develop your application, please explain exactly what, how, which

parameters, etc. so that your results can be reproduced. Submit that description as a separate

document and name it Code_AI.doc (or .pdf).

4. D4: A five-minute video, where you discuss your role in the group, your contribution to the final

submission and the steps that you followed for completing the tasks. This is relevant only for a

project team of at least 2 persons. You can either:

a. make the videos of every group member accessible from outside Turnitin (using external

drives like google drive). Then, please make sure to indicate the links in the project report.

b. Combine it with the rest of the deliverables and submit (see Section Submission format

below).

5. D5: Powerpoint presentation (max 12 slides).

6. D6: Live demo of the project: duration 10 min (roughly 5 min presentation + 5 min questions). [this

is not meant to be submitted]. The date of the presentation will be communicated in due time.

Please note that there is no limit on the word count for both the proposal and the final report. All of

these reports will be evaluated based on their content and not their length. But given the fact that this

is a team project and for your guidance, you may try to go for about 1500 words for the proposal and

about 3000 words for the final report. In the case of individual projects, you may limit your

proposal and final report to 800 and 1500 words respectively. Please note that the presentation,

code and video do not count towards the number of words.

SUBMISSION FORMAT

Except for the proposal, the rest of the deliverables should be submitted through Turnitin (large file box).

Once you have all submission elements, please zip all of them in one file and name it: “Teamname_AI.zip”

and upload in Turnitin.

MARKING CRITERIA

As noted above, there are three parts to this assessment: the technical report of an empirical investigation,

the source code and the final presentation. The following criteria will be used to assess the assignment:

June 2019 v1

Page 3 of 5

Criteria Mark ILO(s)

Quality of the report:

- Complexity of the project

- Clear presentation

- Critical evaluation

- Conclusions and future improvements

- Completeness

40% 1,2,3

Quality of the code which covers the following elements:

- Completeness (all expected steps and functionalities must be

implemented)

- Correct execution of the code

- Documentation of the code

- Demo (part of the presentation) will count towards this criterion

35% 3,4

Quality of the presentation delivery

- Delivery

- Demo

- Questions

25% 1, 2, 3, 4

To get “pass”: you have to observe the conjunction of the following elements:

• Submit all deliverables.

• Define a project of reasonable complexity.

• Provide a decent report and fully running code (maybe using existing AI libraries).

• Deliver a good presentation and answer most questions, showing a good understanding of all

facets of the project.

• Active participation in the development of the project.

To achieve higher mark: you will need to:

• Submit all deliverables.

• Define a project of a good level of complexity.

• Provide an excellent report with details and fully running code with high quality, potentially most of

the code is implemented by yourself.

• Deliver a excellent presentation and answer all questions, showing excellent understanding of all

facets of the project.

• Active participation in the development of the project.

Note please that in case the project is developed as a team, the members of that team may not necessarily

get the same mark. It is based on the contribution and involvement in the execution of the project.

LEARNING OUTCOMES

Having completed this unit, the student is expected to:

1. Demonstrate an understanding of the principal challenges involved in AI, the major research areas,

and the overall historical development of the field.

2. Compare and contrast techniques from the various sub-fields of AI.

3. Demonstrate an understanding of the applicability and limitations of AI for problems in a real-world

context.

4. Implement a solution for a real-world problem using AI techniques and software tools.

QUESTIONS ABOUT THE BRIEF

This assignment will be discussed in class, where students are encouraged to ask questions for clarification.

Feel free to use email when no lab session is scheduled between the time the questions arise and the

submission deadline.

Signature Marker Hamid Bouchachia

June 2019 v1

Page 4 of 5

June 2019 v1

Page 5 of 5

HELP AND SUPPORT

• If a piece of coursework is not submitted by the required deadline, the following will apply:

1. If coursework is submitted within 72 hours after the deadline, the maximum mark that can be

awarded is 40%. If the assessment achieves a pass mark and subject to the overall performance

of the unit and the student’s profile for the level, it will be accepted by the Assessment Board as

the reassessment piece. The unit will count towards the reassessment allowance for the level; This

ruling will apply to written coursework and artefacts only; This ruling will apply to the first attempt

only (including any subsequent attempt taken as a first attempt due to exceptional circumstances).

2. If a first attempt coursework is submitted more than 72 hours after the deadline, a mark of zero

(0%) will be awarded.

3. Failure to submit/complete any other types of coursework (which includes resubmission

coursework without exceptional circumstances) by the required deadline will result in a mark of

zero (0%) being awarded.

The Standard Assessment Regulations can be found on Brightspace.

• If you have any valid exceptional circumstances which mean that you cannot meet an assignment

submission deadline and you wish to request an extension, you will need to complete and submit the

Exceptional Circumstances Form for consideration to your Programme Support Officer (based in

C114) together with appropriate supporting evidence (e.g, GP note) normally before the coursework

deadline. Further details on the procedure and the exceptional circumstances form can be found on

Brightspace. Please make sure that you read these documents carefully before submitting anything

for consideration. For further guidance on exceptional circumstances please see your Programme

Leader.

• You must acknowledge your source every time you refer to others’ work, using the BU Harvard

Referencing system (Author Date Method). Failure to do so amounts to plagiarism which is against

University regulations. Please refer to http://libguides.bournemouth.ac.uk/bu-referencing-harvardstyle

for the University’s guide to citation in the Harvard style. Also be aware of Self-plagiarism, this

primarily occurs when a student submits a piece of work to fulfill the assessment requirement for a

particular unit and all or part of the content has been previously submitted by that student for formal

assessment on the same/a different unit. Further information on academic offences can be found on

Brightspace and from https://www1.bournemouth.ac.uk/discover/library/using-library/howguides/

how-avoid-academic-offences

• Students with Additional Learning Needs may contact Learning Support on

www.bournemouth.ac.uk/als

• You should not be conducting any primary research (i.e. carrying out an investigation to acquire data

first-hand, for example, where it involves approaching participants to ask questions or to participate in

surveys, questionnaires, interviews, observations, focus groups, etc.) unless otherwise specified in the

brief. However, if there is a genuine requirement to collect primary research data you will require ethical

approval before doing so. In the first instance, please discuss with the Unit Leader. The collection of

primary data without appropriate ethical approval is a serious breach of Bournemouth University’s

Research Ethics Code of Practice and will be treated as Research Misconduct.

Disclaimer: The information provided in this assignment brief is correct at time of publication. In the unlikely

event that any changes are deemed necessary, they will be communicated clearly via e-mail and

Brightspace and a new version of this assignment brief will be circulated.