42892 Applied Machine Learning
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 2 cp
Result type: Grade and marks
Requisite(s): 42894 Advanced Machine Learning
Description
Applied Machine Learning develops the autonomy of students to plan and implement a machine learning project using the whole life cycle of such projects from problem understanding to deployment of results. This practical, problem-based subject enables demonstration of machine learning expertise and professional communication of machine learning.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Implement a machine learning project in a business environment. (B.1) |
|---|---|
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
Teaching and learning strategies
Microcredential presentation includes weekly synchronous one-hour online workshops facilitated by an expert UTS academic(s) supporting self-study and online (LMS) learning activities. Case studies of real-world business illustrate applications of machine learning techniques. The workshop sessions focus on hands-on experience in machine learning problems guided by the real world case studies, and the understanding and interpretation of the results. Regular formative quizzes and questions throughout the semester will allow learners to gauge their progress.
Content (topics)
Review and expand on key concepts in Machine Learning through case studies and practical problems:
- Understanding where machine learning fits into business
- Who are the stakeholders of machine learning and what are their perspectives, skills and needs.
- How to contribute high value to the business with machine learning
- scoping business problem
- defining success criteria
- understanding your organization data
- Machine Learning solution development
- Data collection and pre-processing
- Model building and pattern discovery
- Model validation and deployment
- Model monitoring, decay and adaptation
- Insight generation – machine learning outputs and realizing the benefits
- Reports, presentations, prototypes, dashboards.
- Communication of the outcomes of the models to business stakeholders.
- Anticipating the barriers to achieving benefits and how to work with champions to realize success.
Limitations of machine learning and how to communicate that.
Assessment
Assessment task 1: Machine learning project – proof of concept
| Intent: | Demonstrate ability to develop a machine learning project from its onset of business problem definition to generating insights and communicating results. It reinforces the hands-on skill in defining, implementation and evaluation of machine learning project. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 100% |
| Length: | 2,000 words |
Minimum requirements
In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.