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DESCRIPTION OF FINAL ASSESSMENT
Course Code G1109
Course Name Introduction to Machine Learning with Python
Lecturer Goh Sim Kuan
Academic Session 2024/02
Assessment Title Project
A. Introduction/ Situation/ Background Information
This assignment assesses students' ability to develop machine learning applications using Python,
with a focus on human pose estimation. Students will apply Python programming and machine
learning techniques to accurately estimate human poses. The project involves tasks such as data
preprocessing, model training, evaluation, and potentially deployment. Through this assignment,
students demonstrate their practical understanding of machine learning concepts and their
proficiency in Python for solving real-world problems related to human pose estimation.
B. Course Learning Outcomes (CLO) covered
At the end of this assessment, students are able to:
CLO 1 Display the ability to write, test, debug and evaluate Python code. (P2, PLO1)
CLO 2 Apply machine learning algorithms within the constraints of a Python language's
syntax and semantics. (C3, PLO6)
CLO 3 Demonstrate teamwork in solving practical problem using machine learning algorithm.
(A3, PLO11)
C. University Policy on Academic Misconduct
1. Academic misconduct is a serious offense in Xiamen University Malaysia. It can be defined
as any of the following:
i. Plagiarism is submitting or presenting someone else s work, words, ideas, data or
information as your own intentionally or unintentionally. This includes incorporating
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published and unpublished material, whether in manuscript, printed or electronic form into
your work without acknowledging the source (the person and the work).
ii. Collusion is two or more people collaborating on a piece of work (in part or whole) which
is intended to be wholly individual and passed it off as own individual work.
iii. Cheating is an act of dishonesty or fraud in order to gain an unfair advantage in an
assessment. This includes using or attempting to use, or assisting another to use materials
that are prohibited or inappropriate, commissioning work from a third party, falsifying data,
or breaching any examination rules.
All assessments submitted must be the student s own work, without any materials generated by AI
tools, including direct copying and pasting of text or paraphrasing. Any form of academic
misconduct, including using prohibited materials or inappropriate assistance, is a serious offense
and will result in a zero mark for the entire assessment or part of it. If there is more than one guilty
party, such as in case of collusion, all parties involved will receive the same penalty.
D. Instruction to Students
This is a group project, where a team of students implements python, machine learning and deep
learning solutions to real-world problems. Students collaborate, discuss, formulate problems,
develop solutions, document results & findings, and give presentations in the project.
Submission type: Report with a single Jupyter notebook that includes
code, documentation.
Deadline for Project Submission: Week 5, one day before presentation
Only one team leader needs to perform the submission on behalf of the team. Please name the file
using your team s name.
For the presentation, each group is given 10 minutes. No extra time will be given, so please plan
your time wisely.
Peer assessment will be conducted by the end of Week 5.
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E. Evaluation Breakdown
No. Component Title Percentage
(%)
1. Report/ Code 50
2. Presentation 20
3.
4.
5.
TOTAL 70
F. Task(s)
Task A: Create a captivating video showcasing the picturesque landscape of the Xiamen
University Malaysia campus while demonstrating the innovative application of human pose
estimation (HPE). Begin by filming a dynamic 15-30-second video featuring team members
engaging in one of the activities such as dance, kung fu, yoga, and more against the backdrop of
the campus scenery. Subsequently, utilize advanced HPE techniques to accurately extract and
visualize the postures of the participants.
Task B: Please watch the following video from 0:57 to 1:24 and write a python code to count the
number of repetitive movements using the HPE algorithm developed in Task A.
https://www.youtube.com/watch?v=TPbN9qXxowM&ab_channel=XinJ
Report
Please write your report, max 5 pages, containing a precise description of the project. Most
intermediate visualization and analysis should be provided in a jupyter notebook. The report and
presentation should include an Introduction, problem formulation, experiments, results, and
discussion.
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Code and Video
Your code in jupyter notebook should be documented appropriately with explanations and
justification of the analysis performed. The visualization should also be clearly described. . Videos
of the experiment is required for demonstration.
Presentation
Keep the presentation concise on what is actually being accomplished. Every team should
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