Data Mining and Machine Learning COMP 3027J
ASSIGNMENT 1
Weight: 40%
Submissions: A report (PDF), and a zip file (including code and datasets) on Brightspace.
The purpose of this assignment is to practice how to use data mining and machine learning to
solve real-world problems. You will need to identify the target problem yourself. You can
choose any project, but it must be a classification task and includes visual analytics in the
report. (Note: Do not related to or use the dataset in Assignment 2; Do not related to your
FYP project.) as long as it is legal. This assignment is a group project, and each group should
have four members. Each group only needs to submit one solution.
Your pdf report should clearly detail how you carried out the experiment to address your
targeted problem and show the results you got.
1. Your report should be written in Overleaf, and use the provided template:
https://www.overleaf.com/latex/templates/acm-journals-primary-articletemplate/cpkjqttwbshg.
2. It should be a human-readable document (e.g. do not include code)
3. The final report is expected to be 4-6 pages including references.
4. You should provide your UCD student number instead of institution in the provided
template.
5. Use clear headings for each section.
6. Include tables and figures if needed appropriately, such as giving captions, describing
your figures or analysing the results provided in your tables in your text etc.
7. The final report filename should be “Comp3027J_GroupXX” (e.g.
Comp3027J_Group01)
In your report, it is recommended to discuss the following essential topics, but not limited to
these topics:
1. What is the real-world problem addressed and why it is important.
2. Dataset selection (collection) and Data pre-processing.
Where you find your data (or how do you collect the data and create your dataset)?
How do you analyze your data?
how to pre-process your data to fit your solution?
Any challenges with your dataset?
etc.
3. Methodology
Any machine learning algorithm can be used (not limited to the algorithm we have
learned).
Creativity is encouraged.
Be careful, a sophisticated approach with little description and explanation will
receive little credit.
4. Evaluation
Elaborate your experiment, such as splitting dataset, K-fold;
Compare your solution with benchmarks in literature;
Evaluation metrics for your task;
Analysing your results etc.
You should submit a pdf file and a zip file. In your zip file, you should include your code and
dataset. Please make sure to clean up your code to make the results reproducible. If its size
exceeds the Brightspace limit, it needs to be submitted via a USB key. Note your pdf report
must be submitted as an individual file, which should not be compressed into the zip file.
There will be an interview at the end of the term, and you will be asked about the methodology
adopted.
2
• Grading
Problem Literature Methodolgy Evaluation Code+Reproducibility
5% 5% 15% 10% 5%
请加QQ:99515681 邮箱:99515681@qq.com WX:codinghelp
- 全球“最独特”的一台华为 nova 6 5G 版手机是什么样子的?
- 拼多多抖音淘宝京东,谁是真低价?
- 老杨第一次再度抓握住一瓶水,他由此产生了新的憧憬
- 丰田章男称未来依然需要内燃机 已经启动电动机新项目
- B站更新决策机构名单:共有 29 名掌权管理者,包括陈睿、徐逸、李旎、樊欣等人
- 苹果罕见大降价,华为的压力给到了?
- 三明列东又有房子要拆迁!住这里的人要发了!
- 放大招后,广州又忍不住了…
- 私募积极加仓,百亿股票私募仓位指数创出近八周新高
- 他,传闻中马云最想见的人
- 升级的脉脉,正在以招聘业务铺开商业化版图
- 如何经营一家好企业,需要具备什么要素特点
- 智慧驱动 共创未来| 东芝硬盘创新数据存储技术
- 创意驱动增长,Adobe护城河够深吗?
- 全力打造中国“创业之都”名片,第十届中国创业者大会将在郑州召开