COMP 3032 Machine Learning
Credit Points 10
Legacy Code 301435
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Description Machine Learning is one of the most important technologies in the fields of Artificial Intelligence and Data Science used to explain large datasets, inform decisions and highlight risks. Machine Learning is relevant for solving a range of problems in many industries dealing with significant amounts of information and the structure of that information. In this unit, students put Machine Learning theory into action through widely used algorithms and practical applications for designing, training, and evaluating common learning models and systems. Students use Python programming and, as a result, learn about its important machine learning libraries and packages, such as Scikit-Learn, Keras and TensorFlow for solving practical problems and tasks.
School Computer, Data & Math Sciences
Discipline Artificial Intelligence
Student Contribution Band HECS Band 2 10cp
Check your HECS Band contribution amount via the Fees page.
Level Undergraduate Level 3 subject
Pre-requisite(s) MATH 1028 OR
MATH 1033 OR
MATH 1003 OR
MATH 1030 AND
COMP 1005 OR
MATH 1002 OR
COMP 3002
Learning Outcomes
- Apply machine learning tasks such as clustering, dimensionality reduction, regression and classification.
- Apply machine learning workflow when evaluating learning systems for given tasks.
- Determine appropriate machine learning models and algorithms in various scenarios.
- Apply Python programming skills and tools for solving common machine learning application problems.
Subject Content
2.Supervised Learning: Regression and Classification
3.Model Selection
4.Unsupervised Learning: Clustering, Dimensionality Reduction and Manifold Learning
5.Non-parametric methods, Tree based Methods, and Support Vector Machines
6.Deep Learning: Foundations of Neural Networks, Autoencoder, CNNs, GANs
7.Reinforcement Learning
8.Python based machine learning libraries, packages and applications
Assessment
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
| Item | Length | Percent | Threshold | Individual/Group Task |
|---|---|---|---|---|
| Intra-session Exam | 2 hours | 40 | Y | Individual |
| Practicals | 2 hours (per practical) | 15 | N | Individual |
| Practicals | 4 hours (per practical) | 45 | N | Individual |
Prescribed Texts
- Alpaydin, E. (2020). Introduction to machine learning (4th ed.). MIT Press.
Teaching Periods
Spring
Penrith (Kingswood)
Day
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Parramatta - Victoria Rd
Day
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