• Offered by Research School of Computer Science
  • ANU College ANU College of Engineering and Computer Science
  • Classification Advanced
  • Course subject Computer Science
  • Academic career Postgraduate
  • Course convener
    • Dr Cheng Ong
  • Mode of delivery In Person
  • Co-taught Course COMP4670
  • Offered in First Semester 2017
    See Future Offerings

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

On satisfying the requirements of this course, 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

http://machlearn.gitlab.io/isml2017/

Indicative Assessment

  • Assignment 1 (20%)
  • Assignment 2 (20%)
  • Final 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

2 lectures, 1.5 hours each (3 hours total per week, 1 lab session (2 hours) per week, 2 hours independent study per week

Requisite and Incompatibility

To enrol in this course you must be studying a Master of Computing or Master of Applied Data Analytics. You are not able to enrol in this course if you have successfully completed COMP4670.

Prescribed Texts

Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer

Assumed Knowledge

Students are expected to have a background that is equivalent to the prerequisites of COMP4670.

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. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
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
Domestic fee paying students
Year Fee
2017 $3660
International fee paying students
Year Fee
2017 $4878
Note: Please note that fee information is for current year only.

Offerings and Dates

The list of offerings for future years is indicative only

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery
2603 20 Feb 2017 27 Feb 2017 31 Mar 2017 26 May 2017 In Person

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions