Page 1 Kaplan Business School Assessment Outline
Assessment 2 Information
Subject Code: TECH3300
Subject Name: Machine Learning Applications
Assessment Title: Design a Fruit Classification System
Assessment Type: Coding
Assessment Length: 1500 Words (+/-10%)
Weighting: 40%
Total Marks: 40
Submission: MyKBS
Due Date: Week 9
Your Task
Design a fruit classification system to identify the fruit name shown in the image.
Your organisation is helping local farmers with sorting their produce based on the fruit labels on the boxes. It will aid farmers to stack similar products in the same pallet in a lesser amount of time. The starting point for this project is to create a fruit classification system based on the supplied image. As a computer vision expert your team lead asked you to create a fruit classification system.
Data
A fruits dataset is curated by pre-processing the Kaggle Fruit classification dataset (https://www.kaggle.com/datasets/sshikamaru/fruit-recognition/data) and is provided to you in MyKBS. You are encouraged to explore the original source.
The original dataset is pre-processed and is provided in 2 folders - train and test. MyKBS provides you these folders each containing 14 folders each with the respective fruit images.
You are required to train a fruit classification system using the train images. And test the system using the test images.
Problem Statement
As an individual, you are required to download the data, i.e., train and test folders from MyKBS. You must build a fruit classification system to identify similar fruits based on the supplied image. You should systematically approach the problem by addressing the below tasks:
Learning Objectives
This assessment aims to achieve the following subject learning outcomes:
LO1: Explore programming functions to source, store and prepare data for machine learning applications.
LO3: Design algorithmic models for the application of machine learning in information technology.
LO4: Create advanced insights of strategic organisational value with the aid of machine learning.
Assessment Guidelines
You are required to follow the below guidelines:
Academic Integrity and Conduct Policy
https://www.kbs.edu.au/admissions/forms-and-policies
KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.
Please read the policy to learn the answers to these questions:
https://www.kbs.edu.au/admissions/forms-and-policies
Length Limits for Assessments
Penalties may be applied for assessment submissions that exceed prescribed limits.
Study Assistance
Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Further details can be accessed at https://elearning.kbs.edu.au/course/view.php?id=1481
Page 5 Kaplan Business School Assessment Outline
Generative AI Traffic Lights
Please see the level of Generative AI that this assessment is Level 2 has been designed to accept:
| Traffic Light | Amount of Generative Artificial Intelligence (GenerativeAI) usage | Evidence Required | This assessment (✓) |
|---|---|---|---|
| Level 1 Prohibited: No GenerativeAI allowed | This assessment showcases your individual knowledge, skills and/or personal experiences in the absence of Generative AI support. | The use of generative AI is prohibited for this assessment and may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. | |
| Level 2 Optional: You may use GenerativeAI for research and content generation that is appropriately referenced. See assessment instructions for details | This assessment allows you to engage with Generative AI as a means of expanding your understanding, creativity, and idea generation in the research phase of your assessment and to produce content that enhances your assessment. I.e., images. You do not have to use it. | The use of GenAI is optional for this assessment. Your collaboration with GenerativeAI must be clearly referenced just as you would reference any other resource type used. Click on the link below to learn how to reference GenerativeAI. https://library.kaplan.edu.au/referencing-other-sources/referencing-other-sources-generative-ai In addition, you must include an appendix that documents your GenerativeAI collaboration including all prompts and responses used for the assessment. Unapproved use of generative AI as per assessment details during the content generation parts of your assessment may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. Ensure you follow the specific assessment instructions in the section above. | ✓ |
| Level 3 Compulsory: You must use GenerativeAI to complete your assessment See assessment instruction for details | This assessment fully integrates Generative AI, allowing you to harness the technology's full potential in collaboration with your own expertise. Always check your assessment instructions carefully as there may still be limitations on what constitutes acceptable use, and these may be specific to each assessment. You will be taught how to use generative AI and assessed on its use. | Your collaboration with GenerativeAI must be clearly referenced just as you would reference any other resource type used. Click on the link below to learn how to reference GenerativeAI. https://library.kaplan.edu.au/referencing-other-sources/referencing-other-sources-generative-ai In addition, you must include an appendix that documents your GenerativeAI collaboration including all prompts and responses used for the assessment. Unapproved use of generative AI as per assessment details during the content generation parts of your assessment may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. Ensure you follow the specific assessment instructions in the section above. |
Assessment Marking Guide
Marking Criteria _____ / 40
| Marking Criteria | F (Fail) 0 – 49% | P (Pass) 50 – 64% | C (Credit) 65 – 74% | D (Distinction) 75 – 84% | HD (High Distinction) 85 – 100% |
|---|---|---|---|---|---|
| Load, Inspect and pre-process the data. _____ / 6 | Inaccurate way(s) to load/pre-process the data or no attempt made. | Partially able to load/pre-process the data with some errors in both the flows. | Able to load/pre-process the data with some errors in one of the flows. | Correctly load/pre-process the data with no errors. Visualise the data. | Demonstrates creative ways to pre-process to get better results with statistics and information about the data. |
| CNN implementation. _____ / 10 | Python code does not work correctly. | Python code works with errors in the CNN architecture. | Python code works with no errors in the CNN architecture. | Python code works perfectly for CNN with early stopping. CNN is documented with proper comments. | Baseline CNN architecture is created and learning curves, confusion matrix and performance values are displayed. |
| Hyper-parameter optimisation. _____ / 4 | Hyper-parameter optimisation is not done. | Flaws in hyper-parameter optimisation. | Only 1 hyper-parameter is optimised. | At least 2 hyper-parameters are optimised. | At least 2 hyper-parameters are optimised. And the best-hyper-parameters are reported. |
Page 7 Kaplan Business School Assessment Outline
| Marking Criteria | F (Fail) 0 – 49% | P (Pass) 50 – 64% | C (Credit) 65 – 74% | D (Distinction) 75 – 84% | HD (High Distinction) 85 – 100% |
|---|---|---|---|---|---|
| Report. _____ / 20 | The report presented is confusing to the reader. In-text referencing and/or reference list is mostly incorrect or non-existent. Substantial grammatical errors are present that causes the reader to not understand. | The report presented is somewhat clear and logical to the reader. Paragraphing or structure has been attempted with limited success. In-text referencing and the resultant reference list adheres to KBS Harvard Referencing Style, with major errors. Many grammatical errors are present and cause significant confusion for the reader. | The report presented is mainly clear and logical to the reader. Paragraphing and structure have been used well to make the information easy to understand. In-text referencing and reference list adheres to KBS Harvard Referencing Style, with minor errors throughout and the occasional major error. Many grammatical errors are present that cause mild confusion for the reader. | The report presented is clear and logical. Paragraphing and structure have been used well, with the use of headings, to make the information easy to understand. In-text referencing and the resultant reference list adheres to Kaplan Harvard Referencing Style, with only the occasional minor error. Minor grammatical errors are present that do not cause confusion for the reader. | The report presented is exceptionally clear and logical. Paragraphing and structure have been used skilfully, with the use of headings, to make the information easy to understand. In-text referencing and the resultant reference list adheres to Kaplan Harvard Referencing Style, with no errors. No grammatical errors are present. |
Get original papers written according to your instructions and save time for what matters most.
