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 modeling; 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:
On satisfying the requirements of this course, students will have the knowledge and skills to:
- Understand a number of models for supervised, unsupervised, and reinforcement machine learning
- Describe the strength and weakness of each of these models
- Understand the mathematical background 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
Indicative Assessment
Assignment 1 (20%); Assignment 2 (20%); Final Oral Exam (60%)
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
Thirty one-hour lectures
Requisite and Incompatibility
You will need to contact the Research School of Computer Science to request a permission code to enrol in this course.
Prescribed Texts
Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer
Majors
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Students continuing in their current program of study will have their tuition fees indexed annually from the year in which you commenced your program. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and 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). You can find your student contribution amount for each course 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 | Description |
|---|---|---|
| 1994-2003 | $1650 | |
| 2014 | $2952 | |
| 2013 | $2946 | |
| 2012 | $2946 | |
| 2011 | $2946 | |
| 2010 | $2916 | |
| 2009 | $2850 | |
| 2008 | $2592 | |
| 2007 | $2298 | |
| 2006 | $2190 | |
| 2005 | $2190 | |
| 2004 | $2190 |
- International fee paying students
| Year | Fee |
|---|---|
| 1994-2003 | $3234 |
| 2014 | $3762 |
| 2013 | $3756 |
| 2012 | $3756 |
| 2011 | $3756 |
| 2010 | $3750 |
| 2009 | $3426 |
| 2008 | $3426 |
| 2007 | $3426 |
| 2006 | $3426 |
| 2005 | $3288 |
| 2004 | $3234 |
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 3144 | 17 Feb 2014 | 07 Mar 2014 | 31 Mar 2014 | 30 May 2014 | In Person | N/A |