Overview

How noisy sensor measurements can be interpreted in time? How can autonomous systems (in the virtual or in the real world) use their noisy measurements to learn the best course of actions (policy) to take? This topic teaches how to get robots to incorporate uncertainty into estimating and learning from … For more content click the Read More button below.

Topic availabilities

To view topic availabilities, select an availability from the drop down, towards the top right of the screen.

Tuition pattern

To view tuition patterns, select an availability from the drop down, towards the top right of the screen.

Aims

This topic aims to introduce students to the fundamental principles of theory and practice of estimation and machine learning applied on the interpretation of noisy sensor data from any source. The performance of the sensors, including precision, accuracy, repeatability, sensitivity, linearity and dynamic performance, are analysed. Calibration and estimation techniques are also examined. Reinforcement learning and Deep Reinforcement Learning will be introduced as a means to take the students to the state-of-the-art of modern intelligent systems and sensor interpretation.

Assessments

To view assessment information, select an availability from the drop down, towards the top right of the screen.

Current students should refer to FLO for detailed assessment information, including due dates. Assessment information is accurate at the time of publishing.

For policy details, visit Assessments

Requisites information

Pre-requisites:

Assumed knowledge

Students undertaking the one year honours programs should check to make sure they have the appropriate background from their undergraduate degree/s.