COMPX523

Machine Learning for Data Streams

Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental algorithms that process streams under strict resource limitations.

Paper Information

Points: 15.0
Prerequisite(s): COMPX305 or COMPX310 or COMP316 or COMP321 and a further 30 points at 300 level in Computer Science and/or Electrical and Electronic Engineering.
Internal assessment / examination: 100:0
Restriction(s): COMP423, COMP523

Trimesters and Locations

Occurrence Code When taught Where taught
24A (HAM)A Trimester : 26 Feb 2024 - 23 Jun 2024 Hamilton

Timetabled Lectures for Machine Learning for Data Streams (COMPX523)

DayStartEndRoomDates
Thu9:00 AM11:00 AMG.1.15Feb 26 - Jun 2

NB:There may be other timetabled events for this paper such as tutorials or workshops.
Visit the online timetable for COMPX523 for more details


Indicative Fees for Machine Learning for Data Streams (COMPX523)

Occurrence Domestic International
 Tuition Resource 
24A (HAM) $1142 $5168
You will be sent an enrolment agreement which will confirm your fees.
Tuition fees shown below are indicative only and may change. There are additional fees and charges related to enrolment - please see the Table of Fees and Charges for more information.

Paper Outlines

The following paper outlines are available for Machine Learning for Data Streams (COMPX523).
If your paper occurrence is not listed contact the Faculty or School office.

Additional Information

Available Subjects:  Artificial Intelligence | Computer Science | Electrical and Electronic Engineering | Electronic Engineering | Software Engineering

Other available years: Data Stream Mining - COMPX523 (2023) , Data Stream Mining - COMPX523 (2022) , Data Stream Mining - COMPX523 (2021) , Data Stream Mining - COMPX523 (2020) , Data Stream Mining - COMPX523 (2019)

Paper details current as of : 27 February 2024 10:33am
Indicative fees current as of : 27 February 2024 4:32am

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