• Offered by Research School of Computer Science
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Computer Science
  • Areas of interest Computer Science
  • Academic career UGRD
  • Course convener
    • Dr Ulrich Webers
  • Mode of delivery In Person
  • Offered in First Semester 2014
    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 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

http://sml.forge.nicta.com.au/isml.html

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

To enrol in this course you must have completed COMP1100 or COMP1130 or COMP1730; and 6 units of 1000 level MATH courses and 12 units of 3000 level COMP or 12u of MATH courses.

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
Domestic fee paying students
Year Fee
1994-2003 $1650
2004 $2190
2005 $2190
2006 $2190
2007 $2298
2008 $2592
2009 $2850
2010 $2916
2011 $2946
2012 $2946
2013 $2946
2014 $2952
International fee paying students
Year Fee
1994-2003 $3234
2004 $3234
2005 $3288
2006 $3426
2007 $3426
2008 $3426
2009 $3426
2010 $3750
2011 $3756
2012 $3756
2013 $3756
2014 $3762
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
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

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