Artificial Intelligence Project Design and Evaluation
Faculty of Science and Technology - Department of Computing & Informatics Unit Title: Machine Learning Assessment Title: Artificial Intelligence – Project Design & Evaluation Unit Level: 7
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).
- The source code (to solve a chosen problem) that you have implemented, to provide evidence of independent, technical, work (35% of total mark).
- 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:
- The project you are interested in any AI applications: natural language processing and understanding, machine vision, speech recognition, robotics, intelligent agents, smart environments, etc.
- Your teammates (3 people max) – please give a name to your team.
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?
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. In all cases, should you need to discuss your project idea with UL, please make sure to submit a proposal (a brief description that covers the questions mentioned earlier), as soon as you have made your choice by arranging such a discussion preferably before 30/04/2020.
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.
- D1: The proposal to be submitted that includes a brief description that covers the questions mentioned earlier. Deadline is 30/04/2020. Please name it according the name of your team doc (or .pdf) and submit through Turnitin (first box).
- D2: A detailed report that contains the following sections: Front matter, Problem definition, Methodology (all steps), Experiments & discussion, Conclusion and references. Please name it doc (or .pdf).
- D3: Working and well-documented code. Please zip it and name it 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).
- 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:
- 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.
- Combine it with the rest of the deliverables and submit (see Section Submission format below).
- D5: Powerpoint presentation + Demo of the project: duration 20 min + 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. Please note the presentation, code and video do not count towards the number of words.
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.
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:
Quality of the report:
- Complexity of the project
- Clear presentation
- Critical evaluation
- Conclusions and future improvements
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
Quality of the presentation delivery
1, 2, 3, 4
You get “pass” if you achieve 50 marks. Higher marks can be obtained by the combination of the following criteria by higher quality and completeness of the work. 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.
Having completed this unit, the student is expected to:
- Demonstrate an understanding of the principal challenges involved in AI, the major research areas, and the overall historical development of the field.
- Compare and contrast techniques from the various sub-fields of AI.
- Demonstrate an understanding of the applicability and limitations of AI for problems in a real-world context.
Implement a solution for a real-world problem using AI techniques and software tools.