Reference no: EM133925616
Artificial Intelligence & Machine Learning
Assessment Overview - Each week, selected students will lead and facilitate discussions on an assigned AI/ML topic.
Prior to the session, facilitators must prepare and submit one PowerPoint slide containing:
Key points or questions to guide discussion
Any relevant diagrams, data points, or real-world examples
At the end of the session, facilitators will give a 2-minute summary of the key ideas raised by the group.
Case Scenario: Students will prepare discussion questions based on a short, real-world AI case in one of the following sectors:
Healthcare
Banking and Finance
Retail and E-commerce
Manufacturing and Supply Chain
Education
Example Case - Healthcare:
A mid-sized Australian hospital is considering implementing an AI-powered diagnostic tool to speed up patient triage. The system would analyse symptoms, test results, and patient history to prioritise care.
Problem Definition
What problem is the AI tool aiming to solve?
Why is this problem significant for the chosen sector?
AI Solution Overview
Which AI technique(s) would be most appropriate? (e.g., supervised learning, NLP, computer vision)
Justify your choice in business terms.
Benefits and Opportunities
List the top 3 benefits the AI could bring.
How could these benefits be measured?
Risks and Ethical Considerations
Identify main risks (technical, operational, ethical).
Suggest one mitigation strategy per risk.
Implementation Steps
Outline 3-5 key steps to deploy the solution effectively.
Each week selected students will lead and facilitate discussions related to the selected readings. Prior to the session, each Group needs to identify, prepare and submit a PowerPoint slide that discusses a list of issues and/or concepts relevant to the readings that they can use to initiate the discussion with the tutorial group. At the end of the discussion session, the facilitator will give a summary of the ideas or points covered in the discussion and identified by the participants. Therefore, participation in the tutorials is important for the other students and there is a mark for attending the tutorials. Get expert-level assistance in any subject with our assignment help services.
The major challenge associated with this assessment involves being a facilitator and leading discussion among students. All students should search for techniques that demonstrate how to act as a facilitator so as to promote group interaction, dynamics and discussion. There are many techniques that can be used- students should adopt one or even a combination of several to assist them in performing this role.
Proposed Topic Area:
Students can select from these core topics, which align with unit learning outcomes:
Tools for Machine Learning & Artificial Intelligence
(TensorFlow, PyTorch, Scikit-Learn, Azure ML, IBM Watson, OpenAI tools, data preprocessing, model evaluation, hyperparameter tuning, etc.)
Supervised Learning
(Linear/Logistic Regression, Decision Trees, SVM, Ensemble Methods, KNN, Naive Bayes, evaluation metrics, feature engineering, handling imbalanced data, interpretability, hyperparameter optimisation, etc.)
Unsupervised Learning
(K-Means, Hierarchical Clustering, DBSCAN, PCA, anomaly detection, SOMs, association rule learning, etc.)
Deep Learning
(ANN, CNN, RNN, LSTM, GRU, GANs, transfer learning, fine-tuning, deep learning frameworks, etc.)
Natural Language Processing & Computer Vision
(Text preprocessing, embeddings, transformers, attention mechanisms, object detection, segmentation, facial recognition, AR applications, etc.)