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; 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.
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Requisite and Incompatibility
To enrol in this course you must have completed COMP6670 or COMP3670. Incompatible with COMP4670.
Referenced entries (3)
Links and code mentions retain their original context above. They may describe options, prerequisites or exclusions.
- COMP6670 · 2027
- COMP3670 · 2027 · saved source not linked
- COMP4670 · 2027 · saved source not linked
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Describe a number of models for supervised, unsupervised, and reinforcement machine learning
Analyse the strengths and weaknesses of each of these models
Interpret the mathematical equations 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
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 | First Semester | 5420 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |
| 2028 | First Semester | 6692 | 21 Feb 2028 | 28 Feb 2028 | 31 Mar 2028 | 26 May 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 First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 5420 22 Feb 2027 01 Mar 2027 31 Mar 2027 28 May 2027 In Person N/A First Semester Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary 6692 21 Feb 2028 28 Feb 2028 31 Mar 2028 26 May 2028 In Person N/A
Source evidence (2 saved copies)
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assets/Courses/Artificial Intelligence/8000/Statistical Machine Learning/Statistical Machine Learning - ANU.html
Published ANU page · saved page URL
SHA-256: eb7ad2691392a0bf9194c4edeca4e35e3bb1e36f8c887e25376a8a9b8133d380assets/Courses/Data Science/8000/Statistical Machine Learning/Statistical Machine Learning - ANU.html
Published ANU page · saved page URL
SHA-256: 49e6e77e673565ec05335e196699d21aaf68b26655b3e04719a0cf90234d6b17