This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modelling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Describe a number of models for supervised, unsupervised, and reinforcement machine learning
- Assess the strength and weakness of each of these models
- Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
- Implement efficient machine learning algorithms on a computer
- Design test procedures in order to evaluate a model
- Combine several models in order to gain better results
- Make choices for a model for new machine learning tasks based on reasoned argument
Other Information
You should be enrolled in the Master of Applied Data Analytics to undertake this blended intensive course.
Note: Non-MADAN students wanting to enrol are required to seek approval from their Program Convener.
Indicative Assessment
- Exam 1 (20) [LO 1,2,3,7]
- Exam 2 (20) [LO 1,2,3,7]
- Final Exam (60) [LO 1,2,3,5,6,7]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
2 lectures, 1.5 hours each (3 hours total per week, 1 lab session (2 hours) per week, 2 hours independent study per week
Inherent Requirements
N/A
Requisite and Incompatibility
Prescribed Texts
Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer
Assumed Knowledge
Students are expected to have a mathematics background that is equivalent to MATH1014 or MATH1115, and a computer science background equivalent to COMP1110 or COMP1140 or COMP7230 - Intro Prog for Data Scientists .
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $4980 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
