Data-driven decision-making is an essential component of emerging systems that generate and consume very large amounts of sensing data from autonomous vehicles to digital pathology. This course covers technologies and methodologies necessary for inferring useful information and identifying underlying patterns from often raw, incomplete, noisy and corrupted data that is present in real-life applications. It will also give students the opportunity to explore advanced solutions of data analytics such as dimensionality reduction, sparse encoding, compressive sensing, nonlinear filtering, manifold learning, and generative data modelling.
Additional source review: awaiting review. Published wording is shown below.
Saved requirements encoded for automatic checks. These are prototype interpretations, not ANU decisions or degree-completion checks.
Requisite and Incompatibility
To enrol in this course you must be studying: Master of Computing (Advanced) OR Master of Machine Learning and Computer Vision AND have completed 12 units of 6000-level COMP coded courses.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Describe a number of models for inference from data
Assess the strength and weakness of each of these models
Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in the learning models
Implement efficient data analysis algorithms on a computer
Design test procedures in order to evaluate a model
Combine several models in order to gain better results
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 | 5146 | 22 Feb 2027 | 01 Mar 2027 | 31 Mar 2027 | 28 May 2027 | In Person | N/A |
| 2028 | First Semester | 6539 | 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 5146 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 6539 21 Feb 2028 28 Feb 2028 31 Mar 2028 26 May 2028 In Person N/A
Source evidence (1 saved copy)
Matching copies share this record. Their original file locations and checksums are retained.
assets/Courses/Remained/8000/Engineering Data Analytics/Engineering Data Analytics - ANU.html
Published ANU page · canonical URL in the saved page
SHA-256: 3129a85904ed06f1083a0c01b2c8532185ef324f1ac3784880003150e04610b7