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)
| Day | Start | End | Room | Dates |
|---|---|---|---|---|
| Thu | 9:00 AM | 11:00 AM | G.1.15 | Feb 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
Fees for 2024 are not yet available.
Paper Outlines
The following 2023 paper outlines are available for COMPX523. Please contact the Faculty or School office for details on 2024 outlines.
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 : 3 October 2023 8:50am
Indicative fees current as of : 5 October 2023 4:30am