Data-driven decision-making is an essential component of emerging engineering 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.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Describe a number of models for supervised, unsupervised 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 learning algorithms on a computer
- Design test procedures in order to evaluate a model
- Combine several models in order to gain better results
Other Information
See Wattle page
Indicative Assessment
- Assignments (45) [LO null]
- Mid-Term Quiz (25) [LO null]
- Final Exam (30) [LO null]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
The ANU uses 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. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
12 x 2 hr Lectures
12 x1 hr Tutorial
Homework Assignments
Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
N/A
Preliminary Reading
"Pattern Recognition"
"Element of statistical machine Learning"
"Data science"
"Big Data"
Assumed Knowledge
Linear algebra,
Probability
Statistics
Computer programming
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2020 | $4320 |
- International fee paying students
| Year | Fee |
|---|---|
| 2020 | $5760 |
Offerings, Dates and Class Summary Links
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 3075 | 24 Feb 2020 | 02 Mar 2020 | 08 May 2020 | 05 Jun 2020 | In Person | N/A |
