Unit rationale, description and aim
Deep learning drives many real-world AI applications, including computer vision, natural language processing, and large language models (LLMs), and is a core approach within modern artificial intelligence (AI). Graduates, therefore, need the capability to design AI projects responsibly, select appropriate methods, and evaluate solutions that are effective, safe, and aligned with the common good.
This unit develops conceptual and practical skills in contemporary AI, with a strong emphasis on deep learning. Students begin with a recap of core machine learning concepts, then progress to designing and developing deep learning models and exploring other emerging AI technologies. They will work through the full model lifecycle, including development, training, inference, testing, and evaluation. Using tools such as Python and Keras, students implement and assess AI solutions across key areas, including computer vision, natural language processing, large language models (LLMs), anomaly detection, and reinforcement learning. The unit also integrates Microsoft Azure AI Fundamentals to familiarise students with industry standards, design principles, and common AI solution patterns. Throughout, students examine foundational AI use cases that promote stewardship and the common good, with sustained attention to ethics, safety, and human dignity.
This unit aims to equip students with advanced AI knowledge and skills to design, develop, and evaluate AI-enabled applications responsibly, promoting the common good and upholding human dignity.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Demonstrate theoretical knowledge of deep learning...
Learning Outcome 01
Apply practical programming skills, machine learni...
Learning Outcome 02
Critically analyse ethical, social, and technical ...
Learning Outcome 03
Critically analyse the issue of machine bias and i...
Learning Outcome 04
Content
Topics will include:
· Recap of Machine Learning (ML) project design
· Introduction to AI and AI project design
· Introduction to deep learning
· Deep neural networks/Convolutional neural networks
· Deep neural network architectures, training, inference, and testing
· Computer vision
· Natural language processing
· Anomaly detection
· Reinforcement learning
· Microsoft Azure AI Fundamentals
· Fundamental use cases for AI that makes common good and builds better stewardship
· AI ethics and safety with impact on the common good and human dignity
Assessment strategy and rationale
The assessment tasks are deliberately sequenced to scaffold student learning and ensure attainment of the unit learning outcomes and graduate attributes. The initial programming practical provides students with opportunities to apply core machine learning techniques, building foundational technical competence and reinforcing problem‑solving skills. The second assessment extends this by combining question‑answering with programming tasks on essential aspects of AI, requiring students to demonstrate both conceptual understanding and applied skills in a structured, practice‑oriented format. The final project consolidates and advances learning by engaging students in the end‑to‑end development of an AI and machine learning solution, requiring them to integrate technical knowledge with practical programming skills while critically considering issues of AI ethics and safety. This progression from foundational exercises to applied practice to comprehensive project work ensures students develop both technical proficiency and reflective judgement, directly supporting achievement of the unit’s learning outcomes and preparing them for professional challenges in AI and machine learning. The assessments for this unit are designed to demonstrate the achievement of each learning outcome. To pass this unit, you are required to obtain an overall mark of at least 50%.
Overview of assessments
Task 1: Machine learning and AI programming pract...
Task 1: Machine learning and AI programming practical.
The first assessment item consists of several programming tasks. The purpose is to comprehensively assess students’ practical machine learning and AI skills using python and sophisticated machine learning/AI models. The deliverable is python code and solution/algorithm description.
Submission type: Individual
Assessment Method: Practical Tasks
Artefact: Codes and comments in Jupyter Notebook
30%
Task 2: AI-900 Microsoft Azure AI Fundamentals ce...
Task 2: AI-900 Microsoft Azure AI Fundamentals certificate exam
This assessment is a Microsoft certification exam. It evaluates students’ comprehensive knowledge and understanding of AI and machine learning against an industry-recognised standard
Submission type: Individual
Assessment Method: Certification Exam via Certiport Systyem
Artefact: Exam
30%
Task 3: Artificial intelligence and machine learn...
Task 3: Artificial intelligence and machine learning project
The final assessment is a group-based AI project involving machine learning. It is designed to assess students’ overall knowledge and understanding of artificial intelligence and machine learning, as well as their ability to collaboratively design and implement AI solutions. The assessment requires students to address a real-world, complex AI and machine learning problem, with explicit consideration of AI ethics and safety. The group deliverable will comprise both a written report and accompanying code.
Submission type: Group
Assessment Method: project
Artefact: Codes, comments, and summary in Jupyter Notebook Online Viva
40%
Learning and teaching strategy and rationale
This unit is delivered through Attendance and Online modes using a single, integrated learning and teaching strategy designed to ensure equivalent learning outcomes and a comparable learning experience for all students, while supporting diverse learning needs and maximising access.
Across both modes, learning activities are intentionally aligned to the unit learning outcomes and assessment tasks, and are underpinned by active learning, guided engagement with disciplinary knowledge, opportunities for peer interaction, and regular, timely feedback. While the mode of delivery shapes how students participate, the pedagogical intent, expectations and standards remain consistent.
In Attendance mode, students engage in weekly face-to-face classes at designated locations, supported by preparatory activities prior to workshops and opportunities for consolidation following classes. Online learning platforms are used to complement face-to-face teaching through additional resources and learning activities.
In Online mode, students engage with the same core content and learning outcomes through a combination of synchronous and asynchronous activities, including structured discussions and applied learning tasks that support learning in professional contexts.
Representative texts and references
Required text(s)
Aurélien Géron, 2022. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition, O'Reilly Media, Inc. Wei-Ming Lee. Python Machine Learning, Wiley.
Recommended references
Microsoft Learn. (2025). Microsoft Azure AI Fundamentals (AI-900) Study Guide. Wiley.
Bishop, C. M., & Bishop, H. (2024). Deep learning: Foundations and concepts. Springer.
Chollet, F., & Watson, M. (2025). Deep learning with Python (3rd ed.). Manning Publications.
Brownlee, J. (2021). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
Géron, A. (2022). Machine Learning Engineering with Python: A Guide to Building Production-Ready Machine Learning Systems. O’Reilly Media.
Kelleher, J. D., Namee, B., & D’Arcy, A. (2023). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (2nd ed.). MIT Press.
Raschka, S., & Mirjalili, V. (2024). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 (5th ed.). Packt Publishing.
Goodfellow, I., Bengio, Y., & Courville, A. (2024). Deep Learning (2nd ed.). MIT Press.
Jean, H. (2020). Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics. O’Reilly Media
Artasanchez, A., & Joshi, P. (2020). Artificial Intelligence with Python: Your Complete Guide to Building Intelligent Apps Using Python 3.x (2nd ed.). Packt Publishing.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly Media.