- Class Number 7516
- Term Code 3360
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Thang Bui
- Jo Ciuca
- Dr Thang Bui
- Class Dates
- Class Start Date 24/07/2023
- Class End Date 27/10/2023
- Census Date 31/08/2023
- Last Date to Enrol 31/07/2023
Essential foundations for any machine learning application are a basic statistical analysis of the data to be processed, a solid understanding of the mathematical foundations underpinning machine learning as well as the basic classes of learning/adaptation concepts. Those foundations are bundled in this single, introductory course to machine learning in preparation for deeper explorations into the topic, but also as a standalone unit.
Upon successful completion, students will have the knowledge and skills to:
- Develop an appreciation for what is involved in learning from data
- Understand basic data wrangling and data exploration
- Describe a variety of machine learning tasks: clustering, dimensionality reduction, regression and classification
- Understand how to formalise practical problems in terms of above tasks
- Interpret mathematical equations from linear algebra, calculus, statistics, and probability theory in terms of machine learning methods
- Understand how to perform evaluation of learning algorithms and model selection in practical problems
This course will introduce the latest research in machine learning in the first few minutes of most lectures. Besides, insights on how the basic machine learning models underpin critical real-world applications will be integrated in teaching.
Students will be given feedback in the following forms in this course:
- written comments
- verbal comments
- feedback to the whole class, groups, individuals, focus groups etc
Feedback on the first homework assignment will be provided in the first half of the teaching period and before the withdrawal date.
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.
|Summary of Activities
|Introduction and Linear Algebra
|Linear Algebra and Analytic Geometry
|Homework 1 released
|Analytic Geometry and Model Meets Data
|Model Meets Data and Clustering
|Homework 2 released
|Homework 1 due
|Probability and Distributions
|Homework 2 due at the end of the semester break
|Linear Regression and Gradient Descent
|Homework 3 released
|Gaussian Mixture Models
|Homework 3 due. Homework 4 released.
|Principal Component Analysis
|Homework 4 due
|Guest Lectures and Reviews
Tutorial RegistrationANU 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.
|Return of assessment
|Homework Assignment 1
|Homework Assignment 2
|Homework Assignment 3
|Homework Assignment 4
* 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:
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.
Assessment Task 1
Learning Outcomes: 1,5
Homework Assignment 1
This assignment takes up 10% of the course's total mark and will be released in Week 2 and due at the end of Week 5. It has coding and theory parts with equal weights. The content covered is linear algebra and analytic geometry. Marking will be based on technical validity, and partial marks apply to most questions.
Assessment Task 2
Learning Outcomes: 1,3
Homework Assignment 2
This assignment takes up 10% of the course's total mark and will be released in Week 4 and due at the end of the semester break. It has coding and theory parts with equal weights. The content covered is clustering, vector calculus, probability, and distributions. Marking will be based on technical validity, and partial marks apply to most questions.
Assessment Task 3
Learning Outcomes: 1,2,5
Homework Assignment 3
This assignment takes up 10% of the course's total mark and will be released in Week 7 and due at the end of Week 9. It has coding and theory parts with equal weights. The content covered is linear regression and GMM. Marking will be based on technical validity, and partial marks apply to most questions.
Assessment Task 4
Learning Outcomes: 1,3,4,6
Homework Assignment 4
This assignment takes up 10% of the course's total mark and will be released in Week 9 and due at the end of Week 11. It has coding and theory parts with equal weights. The content covered is matrix decomposition and PCA. Marking will be based on technical validity, and partial marks apply to most questions.
Assessment Task 5
Learning Outcomes: 1,2,3,4,5,6
The final exam takes up 60% of the course's total mark. The content includes what is taught in all the lectures. Marking will be based on technical validity, and partial marks apply to most questions. There will be no coding questions.
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.
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. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.
Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:
- Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.
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.
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).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Access and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents all ANU students
machine learning; artificial intelligence
Dr Thang Bui
machine learning; artificial intelligence