SIT720 - Machine Learning
Unit details
| Year | 2025 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online Trimester 2: Burwood (Melbourne), Waurn Ponds (Geelong), Online Trimester 3: Burwood (Melbourne), Waurn Ponds (Geelong) |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Bahareh Nakisa Trimester 2: Ming Liu Trimester 3: Durgesh Samariya |
| Prerequisite: | One of SIT718, SIT731 or SIT771 |
| Corequisite: | Nil |
| Incompatible with: | SIT307 |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week. Weekly meetings. |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour online practical experience (workshop) per week. Weekly meetings. |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
Content
Machine learning is an important tool in analytics, where algorithms iteratively learn from data to uncover hidden insights, without being directly programmed on where to find such information. This unit involves students exploring machine-learning techniques such as data representation, unsupervised learning (clustering and factor analysis) methods, supervised learning (linear and non-linear classification) methods, concepts of suitable model complexity for the problem and data at hand. Students will have the opportunity to apply these techniques in solving real-world problem scenarios presented to them in the unit.
Learning outcomes
Each Unit in your course is a building block towards Deakin's Graduate Learning Outcomes - not all Units develop and assess every Graduate Learning Outcome (GLO).
| ULO | These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: | Alignment to Deakin Graduate Learning Outcomes (GLOs) |
|---|---|---|
| ULO1 | Maintain in-depth knowledge of advances in machine learning, and use this knowledge to explain machine learning techniques and algorithms to a range of technical and non-technical audiences. | GLO1: Discipline-specific knowledge and capabilities GLO2: Communication |
| ULO2 | Explore data using a range of machine learning techniques, evaluate resulting models, and extract and communicate insights from data in real-world scenarios. | GLO1: Discipline-specific knowledge and capabilities GLO3: Digital literacy GLO4: Critical thinking GLO5: Problem solving |
| ULO3 | Justify proposed solutions by evaluating and comparing results from alternative approaches to solving real-world problems and exploring data using machine learning techniques. | GLO1: Discipline-specific knowledge and capabilities GLO4: Critical thinking GLO5: Problem solving |
| ULO4 | Create Python scripts to automate the evaluation and analysis of data using a range of machine learning libraries, techniques, and algorithms. | GLO1: Discipline-specific knowledge and capabilities |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Learning portfolio | Learning portfolio consisting of quiz responses, problem-solving tasks, project work, code, and reports | 100% | Week 12 |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.
Learning resource
The texts and reading list for SIT720 can be found via the University Library.
Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Unit Fee Information
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.