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
Student Contribution Band HECS Band 1 10cp
Check your HECS Band contribution amount via the Fees page.
Level Postgraduate Coursework Level 7 subject
Pre-requisite(s) MATH 7012
Restrictions Students must be enrolled in a postgraduate program.
Fundamentals of computer programming and basic linear algebra.
- 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.
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.
2022 Semester 2
Parramatta - Victoria Rd
Subject Contact Oliver Obst Opens in new window
Attendance Requirements 80% attendance rate is imposed in all core subjects’ due to the nature of class activities that are aligned with subject assessments.