Assessment Brief: ICT5357 Problem-Solving and Decision-Making with Machine Learning Trimester 1, 2025
Assessment Overview
Assessment Task | Type | Weight ing | Length | Due | ULOs Asses sed |
Assessment 1: Supervised Machine Learning Project Implement a machine learning model to perform a simple classification or regression task. Write a report outlining steps taken. | Individual 
| 20% | 1000 (500 words for report + 500 words for model) | Week 4 | ULO1 ULO2 ULO4 |
Assessment 2: Laboratory Practicum Regular invigilated practical tests, implementing some machine learning model or machine learning analysis to solve a problem. | Individual
Invigilated 
| 30% | 2000 words (1200 words for report + 800 words for model) | Weeks 6, 8 | ULO1 ULO2 ULO3 ULO4 |
Assessment 3: Unsupervised Machine Learning Project Perform clustering and dimensionality reduction on a complex dataset and implement a machine learning model to perform a classification/regression task on the reduced dataset. Write a report outlining steps taken. | Group 
| 25% | 3000 words (2000 words for report + 1000 words for model) | Week 7 | ULO1 ULO3 ULO4 |
Assessment 4: Computer Vision Case Study Apply strategic problem-solving in a domain involving visual data analysis. Write a report outlining steps taken. | Individual
| 25% | 2500 words (1500 words for report + 1000 words for model) | Week 12 | ULO1 ULO2 ULO4 |
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:
Format |
I acknowledge the use of [insert the name of AI system and link] to [describe how it was used]. The prompts used were entered on [enter the date in ddmmyyy:] [list the prompts that were used] |
Example |
Tools I acknowledge the use of ChatGPT to create content to plan and brainstorm ideas for my assessment. The prompts used were entered on 18 March, 2023: ● What are some key challenges in running an online business? |
Assessment 1: Supervised Machine Learning Project
Due date: | Week 4 |
Group/individual: | Individual |
Word count/Time provided: | 1000 (500 words for report + 500 words for model) |
Weighting: | 20% |
Unit Learning Outcomes: | ULO-1, ULO-2, ULO-4 |
Assessment 1 Detail
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.
Marking Criteria | Not satisfactory (0-49%) of the criterion mark | Satisfactory (50-64%) of the criterion mark | Good (65-74%) of the criterion mark | Very Good (75-84%) of the criterion mark | Excellent (85-100%) of the criterion mark |
Problem Solving (20 marks) | Unable to identify and connect problems to machine learning models and training data. | Able to identify and connect some problems to machine learning models and training data. | Able to identify and connect many problems to machine learning models and training data. | Able to identify and connect problems to machine learning models and training data. | Able to identify and connect problems to machine learning models and training data, and provide clear explanations for these connections. |
Data (20 marks) | Explanation of the dataset is missing or contains significant inaccuracies. No modifications or pre processing are performed, or explanations are unclear. | Dataset is described with basic details but lacks clarity or depth. Basic modifications or pre-processing steps are mentioned, but their rationale is unclear. | Explanation provides a clear overview of the dataset, including features, target variable, and relevance to the task. Modifications or pre-processing are adequately explained and justified. | Explanation is thorough and demonstrates a strong grasp of dataset characteristics and relevance. Modifications or pre-processing are well-reasoned and effectively contribute to model development. | Explanation is exceptional, offering detailed insights into features, target variable, and dataset’s role in the task. Modifications or pre- processing are exceptional, showing deep understanding of their impact on the task. |
Machine Learning (20 marks) | Inappropriate algorithms or hyperparameters are chosen, or explanations are missing. | Basic algorithms are chosen, but their suitability and hyperparameters are inadequately explained. | Appropriate algorithms are selected, relevance to the task is justified with reasonable hyperparameters. | Algorithms are well suited to the task, and hyperparameters are carefully tuned with clear reasoning. | Algorithm selection and hyperparameter tuning are exceptional, showing deep understanding of model choices. |
Models (20 marks) | No model results provided or results do not address the problem. | Basic model results are mentioned but lack context or interpretation. | Provides some meaningful insights, and results are described clearly. | Model outcomes are explained with insight, their implications are well interpreted. | Model performance is explained clearly, interpretation shows deep understanding. |
Clarity and Presentation (20 marks) | Answers are unclear, disorganised, and difficult to understand. | Answers are somewhat clear, but lack organisation. | Answers are mostly clear and organised. | Answers are clear and organised. | Answers are clear and organised, easy to understand, and engaging or insightful. |
Assessment 2: Laboratory Practicum Due date: | Weeks 6, 8 |
Group/individual: | Individual |
Word count/Time provided: | 2000 words (1200 words for report + 800 words for model), 1000 words each |
Weighting: | 2x 15% = 30% |
Unit Learning Outcomes: | ULO-1, ULO-2, ULO-3, ULO-4 |
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%.
Marking Criteria | Not satisfactory (0- 49%) of the criterion mark | Satisfactory (50-64%) of the criterion mark | Good (65-74%) of the criterion mark | Very Good (75-84%) of the criterion mark | Excellent (85-100%) of the criterion mark |
Problem Solving (25 marks) | Unable to identify and connect problems to machine learning models and training data. | Able to identify and connect some problems to machine learning models and training data. | Able to identify and connect many problems to machine learning models and training data. | Able to identify and connect problems to machine learning models and training data. | Able to identify and connect problems to machine learning models and training data, and provide clear explanations for these connections. |
Data and Machine Learning (25 marks) | No relevant data preprocessing, data visualisations, clustering, dimensionality reduction or time series techniques are used. | Some relevant data preprocessing, data visualisations, clustering, dimensionality reduction or time series techniques, but submission contains errors | All/most relevant data preprocessing, data visualisations, clustering, dimensionality reduction or time series techniques are used and explained. | All relevant data preprocessing, data visualisations, clustering, dimensionality reduction or time series techniques are used and explained well. | All relevant data preprocessing, data visualisations, clustering, dimensionality reduction or time series techniques are used and explained with excellent insight. |
| | | or omissions in explanations. | | | |
Orange / AutoML techniques (25 marks) | Machine learning project is disorganised and difficult to follow. | Machine learning project achieves goals but with little organisation. | Machine learning project is somewhat organised but contains one or more errors in the workflow. | Machine learning project is well organised and easy to follow. | Machine learning project is exceptionally organised with a logical workflow. |
Clarity and Presentation (25 marks) | Answers are unclear, disorganised, and difficult to understand. | Answers are somewhat clear, but lack organisation. | Answers are mostly clear and organised. | Answers are clear and organised. | Answers are clear and organised, easy to understand, and engaging or insightful. |
Assessment 3: Unsupervised Machine Learning Project
Due date: | Week 7 |
Group/individual: | Group |
Word count/Time provided: | 3000 words (2000 words for report + 1000 words for model) |
Weighting: | 25% |
Unit Learning Outcomes: | ULO1, ULO3, and ULO4 |
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.
Marking Criteria | Not satisfactory (0- 49%) of the criterion mark | Satisfactory (50-64%) of the criterion mark | Good (65-74%) of the criterion mark | Very Good (75-84%) of the criterion mark | Excellent (85-100%) of the criterion mark |
Problem Solving (10 marks) | Unable to identify and connect problems to machine learning models and training data. | Able to identify and connect some problems to machine learning models and training data. | Able to identify and connect many problems to machine learning models and training data. | Able to identify and connect problems to machine learning models and training data. | Able to identify and connect problems to machine learning models and training data, and provide clear explanations for these connections. |
Data (10 marks) | Explanation of the dataset is missing or contains significant inaccuracies. No modifications or pre processing are performed, or explanations are unclear. | Dataset is described with basic details but lacks clarity or depth. Basic modifications or pre-processing steps are mentioned, but their rationale is unclear. | Explanation provides a clear overview of the dataset, including features, target variable, and relevance to the task. Modifications or pre-processing are adequately explained and justified. | Explanation is thorough and demonstrates a strong grasp of dataset characteristics and relevance. Modifications or pre-processing are well-reasoned and effectively contribute to model development. | Explanation is exceptional, offering detailed insights into features, target variable, and dataset’s role in the task. Modifications or pre- processing are exceptional, showing deep understanding of their impact on the task. |
Data Visualisation (15 marks) | Creates little or no visualisations of data, and/or offers no explanation or understanding of their significance. | Creates visualisations of data, but offers on limited explanation of how these visualisations connect to insights into the problem. | Creates relevant visualisations of data. Presents some insights based on these visualisations. | Creates relevant and helpful visualisations of data. Presents good insights based on these visualisations. | Clearly explains how visualisations of data connect to insights into the problem space. |
Dimensionality Reduction (15 marks) | No dimensionality reduction techniques are used and/or no rationale given for these. | Some dimensionality reduction techniques are used. Little rationale given for these. | Explains some dimensionality reduction techniques used and offers some rationale for these. Shows some insight into dimensionality reduction. | Explains dimensionality reduction techniques used and the rationale for these. Shows good insight into dimensionality reduction. | Clearly explains dimensionality reduction techniques used and the rationale for these. Shows excellent insight into dimensionality reduction. |
Machine Learning (10 marks) | Inappropriate algorithms or hyperparameters are chosen, or explanations are missing. | Basic algorithms are chosen, but their suitability and hyperparameters are inadequately explained. | Appropriate algorithms are selected, relevance to the task is justified with reasonable hyperparameters. | Algorithms are well-suited to the task, and hyperparameters are carefully tuned with clear reasoning. | Algorithm selection and hyperparameter tuning are exceptional, showing deep understanding of model choices. |
Models (10 marks) | No model results provided or results do not address the problem. | Basic model results are mentioned but lack context or interpretation. | Provides some meaningful insights, and results are described clearly. | Model outcomes are explained with insight, their implications are well interpreted. | Model performance is explained clearly, interpretation shows deep understanding. |
Orange / AutoML techniques (15 marks) | Machine learning project is disorganised and difficult to follow. | Machine learning project achieves goals but with little organisation. | Machine learning project is somewhat organised but contains one or more errors in the workflow. | Machine learning project is well organised and easy to follow. | Machine learning project is exceptionally organised with a logical workflow. |
Clarity and Presentation (15 marks) | Answers are unclear, disorganised, and difficult to understand. | Answers are somewhat clear, but lack organisation. | Answers are mostly clear and organised. | Answers are clear and organised. | Answers are clear and organised, easy to understand, and engaging or insightful. |
Assessment 4: Computer Vision Case Study
Due date: | Week 12 |
Group/individual: | Individual |
Word count/Time provided: | 2500 words (1500 words for report + 1000 words for model) |
Weighting: | 25% |
Unit Learning Outcomes: | ULO-1, ULO-2, ULO-3, ULO-4 |
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.
Marking Criteria | Not satisfactory (0- 49%) of the criterion mark | Satisfactory (50-64%) of the criterion mark | Good (65-74%) of the criterion mark | Very Good (75-84%) of the criterion mark | Excellent (85-100%) of the criterion mark |
Strategic Problem Solving (20 marks) | No relevant scenario or problem domain is identified. No relevant machine learning objective is identified or explained. | Some identification of the scenario and problem domain. An objective of the machine learning project is identified, but not explained. | Adequate explanation of the scenario and problem domain. An objective of the machine learning project is identified, but may not be well | Good explanation of the scenario and problem domain. An appropriate machine learning project objective is identified and explained. | Excellent explanation of the scenario and problem domain, and the objective of the machine learning project. |
| | | | explained. | | |
Data (20 marks) | Does not explain how the image library connects to the machine learning objective. | Able to somewhat explain the connection between the image library and the objective of the machine learning project, but no mention of how further data should be collected or augmented. | Able to somewhat explain the connection between the image library and the objective of the machine learning project. Shows little knowledge of how further data should be collected or augmented. | Able to explain the connection between the image library and the objective of the machine learning project. Shows some knowledge of how further data should be collected or augmented. | Able to clearly explain the connection between the image library and the objective of the machine learning project. Shows great insight into how further data should be collected or augmented. |
Image Processing (20 marks) | No image pre processing is performed. | Basic image pre processing is performed, but with no explanation. | Image pre processing is mostly appropriate to the task, but with little explanation. More advanced image pre-processing is mentioned but not explained. | Image pre processing is performed and adequately explained. More advanced image pre-processing is adequately explained in relation to AutoML tools. | Image pre processing is completely appropriate and clearly explained, showing deep understanding of its role in image analysis. More advanced image pre-processing is clearly explained in relation to AutoML tools. |
Models (10 marks) | No model results provided or results do not address the problem. | Basic model results are mentioned but lack context or interpretation. | Provides some meaningful insights, and results are described clearly. | Model outcomes are explained with insight, their implications are well interpreted. | Model performance is explained clearly, interpretation shows deep understanding. |
Orange / AutoML techniques (15 marks) | Machine learning project is disorganised and difficult to follow. | Machine learning project achieves goals but with little organisation. | Machine learning project is somewhat organised but contains one or more errors in the workflow. | Machine learning project is well organised and easy to follow. | Machine learning project is exceptionally organised with a logical workflow. |
Clarity and Presentation (15 marks) | Answers are unclear, disorganised, and difficult to understand. | Answers are somewhat clear, but lack organisation. | Answers are mostly clear and organised. | Answers are clear and organised. | Answers are clear and organised, easy to understand, and engaging or insightful. |
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