• 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 Christian Walder
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in First Semester 2020
    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:

  1. Describe a number of models for supervised, unsupervised, and reinforcement machine learning
  2. Assess the strength and weakness of each of these models
  3. Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
  4. Implement efficient machine learning algorithms on a computer
  5. Design test procedures in order to evaluate a model
  6. Combine several models in order to gain better results
  7. Make choices for a model for new machine learning tasks based on reasoned argument

Other Information

https://machlearn.gitlab.io/sml2019/

Indicative Assessment

  1. Assignment 1 (20) [LO null]
  2. Assignment 2 (20) [LO null]
  3. Final Exam (60) [LO null]

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

Not applicable

Requisite and Incompatibility

To enrol in this course you must have completed COMP3670, or you must have completed all of the following: COMP1110 or COMP1140 and MATH1014 or MATH1115. Incompatible with COMP8600.

Prescribed Texts

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

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:
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
2020 $4320
International fee paying students
Year Fee
2020 $5760
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
2291 24 Feb 2020 02 Mar 2020 31 Mar 2020 29 May 2020 In Person N/A

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