Inrecent top machine learning conferences
| COMP1801 (2021/22) | Machine Learning | Contribution: 100% of course | |
|---|---|---|---|
Feedback and grades are normally made available within 15 working days of the coursework deadline |
|||
Learning Outcome: |
|||
•A report
oFile format: a single PDF document generated by LaTeX. As a LaTeX compiler, we recommend Overleaf, but you can use any LaTeX compiler.oTemplate: You MUST use the latest template uploaded on COMP-1801's Moodle site.
oThe title and abstract MUST NOT be on an independent page. oMargin: follow the template.
oAll equations and tables MUST be generated by LaTeX.
•Any submission that does not follow the specified format will have a zero score without any feedback.
oExamples of submissions that do not follow the format: ▪A paper copy.
▪A PDF file that violates the page limitation or margin, font size, and page size specification.
▪A PDF file that contains screenshots.
▪ LaTeX has been regarded as de-facto standard software to generate
academic documents with equations, such as machine learning papers. In
is also in line with this requirement in this area.
• There are limits on the file size (see the relevant course Moodle page).
Designing a machine learning solution requires considering several
aspects of the problem, the nature of the problem addressed, model
choice,
evaluation among others. It is important for our students to be up to
date with current practices and Machine Learning techniques used in
the
modern software that drives many computers and devices today and be
familiar with their strengths and limitations. Adding these skills to
their portfolio will increase the employability of our graduates and
will help them to aim for higher paying jobs in industry, as well as
academia.
This coursework is an individual practical project that consists of
• A topic that is covered by the lecture.
• A topic that is not covered by the lecture but related to machine learning.
2. Written Report (4 pages of a detailed description of the steps taken in carrying out the project):
• Must follow the template uploaded on the Moodle website.
• Should include references (citing other work) where appropriate (when images, data, code, or any other resources have been used from other sources)
• For document structure, please follow the template.
| Undergraduate Criteria | |
|---|---|
Meets all criteria. Shows a significant amount of critical analysis and exhibits an excellent understanding of the relevant issues. Meets most of the criteria. Demonstrates clear awareness of relevant issues with a high standard of critical analysis. |
|
| Some of the criteria present but is mainly factual and descriptive with little grasp of analysis. | |
|
Some of the criteria present and establishes a few relevant points but superficial or confused exposition of issues. |
|
Little or no evidence of given criteria and no grasp of analysis. Does not demonstrate self-direction, originality in problem solving or a critical self-evaluation of the coursework process. |
The logicality of the explanation is assessed in terms of
• Positioning their work appropriately in the problem domain
• Giving scientific reasons why their method can solve the problem • Giving clear reasons why their evaluation method can judge whether their approach has succeeded or not
• Fair, objective, and reasonable conclusions and critical review • Logical reasoning for other explanations