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.
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Requisite and Incompatibility
To enrol in this course you must: be enrolled in Master of Computing (Advanced) OR have completed or be currently enrolled in (COMP6710 or COMP7710 or COMP6730) OR have completed COMP1110 or COMP1140. Incompatible with COMP3670.
Referenced entries (6)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP6710 · 2027
- COMP7710 · 2027
- COMP6730 · 2027
- COMP1110 · 2027 · saved source not linked
- COMP1140 · 2027 · saved source not linked
- COMP3670 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Develop an appreciation for what is involved in learning via data-driven approaches, like data collection, data safety and privacy, and ethics in machine learning.
Describe a variety of machine learning tasks, like regression and classification, clustering, dimensionality reduction.
Understand how to formalise practical problems and evaluate model performance of machine learning tasks.
Interpret mathematical equations from linear algebra, calculus, statistics, probability theory and related mathematical topics in terms of machine learning methods.
Design mathematical and statistical models using linear algebra, calculus, statistics, probability theory and related mathematical topics in terms of machine learning methods.
Analyse advanced machine learning technologies including reinforcement learning, neural networks, and large models
Published offerings
Future offerings are indicative. Only recognised 2027 sessions with a published class number are offered in the enrolment demo.
| Year | Session | Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|---|---|
| 2027 | Second Semester | 10094 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |
| 2028 | Second Semester | 10855 | 24 Jul 2028 | 31 Jul 2028 | 31 Aug 2028 | 27 Oct 2028 | In Person | N/A |
Complete offering text
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. 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. 2027 2028 Second Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 10094 26 Jul 2027 02 Aug 2027 31 Aug 2027 29 Oct 2027 In Person N/A Second Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 10855 24 Jul 2028 31 Jul 2028 31 Aug 2028 27 Oct 2028 In Person N/A
Source evidence (2 saved copies)
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assets/Courses/Artificial Intelligence/6000/Introduction to Machine Learning/Introduction to Machine Learning - ANU.html
Published ANU page · saved page URL
SHA-256: e38b4b22216ba5189ffe5bb47547a5ad2420db9b4de1a913e0563cc85efa9c9eassets/Courses/Data Science/6000/Introduction to Machine Learning/Introduction to Machine Learning - ANU.html
Published ANU page · saved page URL
SHA-256: dba604c51acd846ae5b82c4d22c25bbc11d43a82aba2d34518d5cc8087f5fd84