Assessment Brief: ICT5357 Problem-Solving and Decision-Making with Machine Learning Trimester 1, 2025

Assessment Overview

equiv. – equivalent word count based on the Assessment Load Equivalence Guide. It means this assessment is equivalent to the normally expected time requirement for a written submission containing the specified number of words.

Note for all assessments tasks:

• ● Students can generate/modify/create text generated by AI. They are then asked to modify the text according to the brief of the assignment.
• ● During the preparation and writing of an assignment, students use AI tools, but may not include any AI-generated material in their final report.
• ● AI tools are used by students in researching topics and preparing assignments, but all AI-generated content must be acknowledged in the final report as follows:

You will be provided with a number of simple raw datasets that could be used for classification or regression. You will choose one dataset and submit a report that outlines:

• ● A problem that could be addressed with the dataset
• ● A machine learning algorithm that could be used to train AI to answer this question
• ● Any preprocessing of data and other preparation that you would do for this training

You will also submit one or more models trained with the data. You are required to submit:

• 1. A document (1000 words) that includes:
• ○ What kind of problem could be solved and/or what kind of decisions could be made, if an appropriate machine learning model were trained on this dataset
• ○ What such a model would do, either as a classification task or a regression task
• ○ An explanation of how the dataset relates to the classification or regression task
• ○ Any specific learning algorithms that might be particularly suitable or unsuitable, given the dataset, and why
• ○ What preprocessing would you perform on the dataset, prior to machine learning
• ○ An evaluation of the performance of the model
2. The project used to develop your model(s).
• 3. The deployed model(s).

Assessment 1 Marking Criteria and Rubric

The assessment will be marked out of 100 and will be weighted 20% of the total unit mark.

Assessment 2: Laboratory Practicum

Assessment 2 Detail

During one hour of the weekly seminar in Weeks 6 and 8, you will be provided with some problem or decision that can be addressed with an appropriate machine learning approach. You will be required to provide written answers, and in some weeks you will also be required to provide one or more machine learning models to address the problem.

These assessments are held during two relevant weekly seminar and they are invigilated.

Assessment 2 Marking Criteria and Rubric

Each assessment will be marked out of 100 and will be weighted 15% of the total unit mark, totalling a combined weight of 2 x 15% = 30%

Assessment 3 Detail

You will be provided with a number of complex high-dimensional raw data sets. You will choose one of these datasets and submit a report that outlines:

• ● A problem that could be addressed with the dataset
• ● How methods could be used to reduce the dimensionality of the dataset and uncover structure in the dataset
• ● What kind of classification or regression could be performed with the data once the complexity in the dataset has been reduced

You will also submit several models that have been trained with the data, including:

• ● A model trained on the raw dataset
• ● A model trained on the dataset after applying some dimensionality reduction techniques
• ● A model trained on the dataset after manually choosing a selection of input features in the training dataset

You are required to submit:

• 1. A document (3000 words) that includes:
• ○ What kind of problem could be solved and/or what kind of decisions could be made, if an appropriate machine learning model were trained on this dataset
• ○ What such a model would do, either as a classification task or a regression task
• ○ An explanation of how the dataset relates to the classification or regression task
• ○ Insights gained from the raw data, including from visualisations of the data
• ○ An explanation of any dimensionality reduction techniques that could be used to reduce the complexity of the raw data
• ○ Any specific learning algorithms that might be particularly suitable or unsuitable, given the dataset, and why
• ○ What preprocessing would you perform on the dataset, prior to machine learning
• ○ An evaluation of the performance of the models
• 2. The project used to develop your models.
• 3. The deployed models.

Assessments 3 Marking Criteria and Rubric

The assessment will be marked out of 100 and will be weighted 25% of the total unit mark.

Assessment 4 Detail

You will be provided with a number of image libraries. You will choose one image library and submit a report that outlines:

• ● A problem that could be solved with the image dataset
• ● How AutoML tools may help to analyse the imagery
• ● A strategy to solve the problem with the image library and an appropriate machine learning model

You will also submit one or more models that you have trained. You are required to submit:

• 1. A document (2000 words) that includes:
• ○ A realistic fictional scenario in which the image library could help solve a problem
• ○ An outline of a strategic approach to solving the problem with the use of the image library and a machine learning model trained from it
• ○ Explorations and insights gained from the image library
• ○ Image preprocessing used on the data, including any advanced pre-processing that might be conducted with access to advanced AutoML tools
• ○ An evaluation of the performance of any models
• 2. The project used to develop your model(s)
• 3. The deployed model(s).

Assessments 4 Marking Criteria and Rubric

The assessment will be marked out of 100 and will be weighted 25% of the total unit mark.