代写G1109编程、代做Python设计程序

日期：2024-03-14 09:56

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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

OFFICE OF ACADEMIC AFFAIRS

Reference No. :  - -V3.0

Effective Date : 1 JUNE 2023

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published and unpublished material, whether in manuscript, printed or electronic form into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 present

within the 10 min given. No extra time will be given, so please plan your time wisely.

APPENDIX 1

MARKING RUBRICS

Component

Title Code and Report Percentage

(%) 50

Criteria

Score and Descriptors

Weight

(%) Marks Excellent

(10-9)

Good

(8-7)

Average

(6-5)

Need

Improvement

(4-3)

Poor

(2-0)

Machine

Learning

knowledge

and

understandi

ng

Students

demonstrate

excellent

technical &

practical

understanding

of machine

learning

problems and

methods.

Students

demonstrate a

good practical

understanding

of machine

learning

problems and

methods.

Students

demonstrate an

average

understanding

of machine

learning

problems and

methods.

Students

demonstrate an

insufficient

understanding

of machine

learning

problems and

methods.

Students fail to

demonstrate an

understanding

of

learning

machine

problems and

methods.

25

The quality

of Code &

Report

Excellent

programming

practice is

followed with

very detailed

documentation.

The report

provides a

concise

summary of

what is

accomplished

with very

detailed

explanations

and discussion.

Good

programming

practice is

followed by

documentation.

The report

provides a

concise

summary of

what is

accomplished

with detailed

explanations

and discussion.

Good

programming

practice is

followed by

simple

documentation.

The report

provides a

summary of

what is

accomplished

with

explanations

and discussion.

Programming

practice is

followed by

simple

documentation.

The report

provides a

summary of

what is

accomplished

with little

explanation

and discussion.

Programming

practice is not

followed with

documentation.

The report

provides an

unclear

summary of

what is

accomplished

with little

explanation

and discussion.

25

TOTAL 50

Component

Title Presentation Percentage

(%) 20

Criteria

Score and Descriptors

Weight

(%) Marks Excellent

(10-9)

Good

(8-7)

Average

(6-5)

Need

Improvement

(4-3)

Poor

(2-0)

Content

Delivery

and Q&A

Project content

was delivered

clearly with

strong

findings.

Students can

Project content

was delivered

clearly with

findings.

Students can

answer

Project content

was delivered

clearly with

findings.

Students can

answer most

Project content

was delivered

clearly with

inferior

findings.

Students can

Project content

was not

delivered

clearly with

inferior

findings.

10

answer all

questions by

the lecturer

and students.

questions by

the lecturer

and students.

questions by

the lecturer

and students.

answer most

questions by

the lecturer

and students.

Students

cannot answer

most questions

by the lecturer

and students.

Teamwork

& Peer

Assessment

All team

worked well

together to

achieve

objectives.

Each member

contributed in

a valuable

way to the

project since

the group was

formed.

Almost all

team worked

well together to

achieve

objectives.

Each member

contributed in

a valuable

way to the