• Class Number 2219
  • Term Code 3330
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Lexing Xie
    • Dr Yuan-Sen Ting
  • Class Dates
  • Class Start Date 20/02/2023
  • Class End Date 26/05/2023
  • Census Date 31/03/2023
  • Last Date to Enrol 27/02/2023
SELT Survey Results

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:

  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

Research-Led Teaching

The course material include recent developments and research trends in machine learning. There will also be guest lectures from academics and industry.

Field Trips


Additional Course Costs


Examination Material or equipment


Required Resources

Up to date class schedule for 2023 is published here https://docs.google.com/spreadsheets/d/e/2PACX-1vQyNSgOwxe0Y-jXumMfudZFT8s2yedsSzx_VNer329rSTLhLBNLxHt4Przr2AKclewJ_Zeik9P5vZbl/pubhtml?widget=true&headers=false.

Class webpage will be announced and linked to wattle.

Whether you are on campus or studying remotely, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Course intro, ML 101 and model selection
2 Linear models for regression and bayesian regression
3 Linear models for regression and bayesian regression
4 Expectation maximisation; Generalisation Quiz 1
5 Neural networks and component analysis
6 Kernel methods Assignment 1 due
7 Gaussian processes
8 Graphical models Quiz 2
9 Sampling
10 Guest lecture Assignment 2 due
11 Research content, course review Video assignment due

Tutorial Registration

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.

Assessment Summary

Assessment task Value Learning Outcomes
Assignment 1 18 % Assignment 1 (Learning outcome 1-6)
Assignment 2 18 % Assignment 2 (Learning outcome 1-7)
Video Assignment 20 % Video Assignment (Learning outcome 1-4, 6-7)
Quizzes 4 % Quiz 1 and Quiz 2 (Learning outcome 1, 2, 3, 5)
Final Exam 40 % Final Exam (Learning outcome 1-7)

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.




A final exam will be held in the examination period.

Assessment Task 1

Value: 18 %
Learning Outcomes: Assignment 1 (Learning outcome 1-6)

Assignment 1

Assessment Task 2

Value: 18 %
Learning Outcomes: Assignment 2 (Learning outcome 1-7)

Assignment 2

Assessment Task 3

Value: 20 %
Learning Outcomes: Video Assignment (Learning outcome 1-4, 6-7)

Video Assignment

Assessment Task 4

Value: 4 %
Learning Outcomes: Quiz 1 and Quiz 2 (Learning outcome 1, 2, 3, 5)


Assessment Task 5

Value: 40 %
Learning Outcomes: Final Exam (Learning outcome 1-7)

Final Exam

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records.

Hardcopy Submission

Not applicable.

Late Submission

This policy applies to Assignment 1, Assignment 2, and the video assignment:

Assignment submission that are late from 1 min to 24 hours attract a 5% penalty (of possible marks available). Submissions late by more than 24 hours will not be accepted.

There will be NO late period for either quiz. Special consideration requests will also NOT be accepted, due to the rapid feedback cycle and redeemable nature of the quizzes. The quiz will be redeemable with final exam, i.e. score for each quiz is calculated as Qx’ = max(Qx, Final), where Qx is the raw quiz score for Quiz 1 (Q1) or Quiz 2 (Q2), out of 100. Final is the score for the final exam, out of 100.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Prof Lexing Xie

Research Interests

(LX) Machine learning, social media, humanising machine intelligence. (YT) Machine learning for astronomy.

Prof Lexing Xie

By Appointment
Dr Yuan-Sen Ting

Research Interests

Dr Yuan-Sen Ting

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