INFO 7001 Advanced Machine Learning
Credit Points 10
Legacy Code 301119
Coordinator Oliver Obst Opens in new window
Description Advanced Machine Learning explores modern methods of classification, clustering and regression to make predictions and analyse different forms of data. Issues that face all machine learning methods, such as model evaluation, assessment and generalisation will also be analysed.
School Computer, Data & Math Sciences
Discipline Information Technology, Not Elsewhere Classified.
Student Contribution Band HECS Band 1 10cp
Level Postgraduate Coursework Level 7 subject
Pre-requisite(s) COMP 7024
Students must be enrolled in a postgraduate program.
Fundamentals of computer programming and basic linear algebra.
On successful completion of this subject, students should be able to:
- Describe appropriate machine learning methods for given problems.
- Fit modern machine learning models to data.
- Make predictions based on a fitted machine learning model.
- Analyse data based on a fitted machine learning model.
- Evaluate the utility of a machine learning method for given data.
1. Introduction to Machine Learning
2. Support Vector Machines
3. Neural Networks
4. Reinforcement Learning
5. Manifold/Metric Learning
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.
|Online quizzes||5x60 minutes||20||N||Individual|
|Project presentation||15 mins||30||N||Individual|
- Sutton, R. S., Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). Cambridge, Massachusets: The MIT Press.
Parramatta - Victoria Rd
Subject Contact Oliver Obst Opens in new window