Key information
- Faculty
- Faculty of Mathematical and Physical Sciences
- Teaching department
- Earth Sciences
- Credit value
- 15
- Restrictions
-
N/A
- Timetable
-
Alternative credit options
There are no alternative credit options available for this module.
In this new module we will introduce recent open source AI methods (Gaussian process, Deep Learning, etc) implemented in python and used in the remote sensing community to optimise the use of remote sensing observations and illustrate these within a wider range of test cases that are relevant in the context of our various undergraduate and postgraduate streams in Earth Sciences and more widely to MAPS faculty students.
Module deliveries for 2024/25 academic year
Intended teaching term:
Term 2 听听听
Undergraduate (FHEQ Level 6)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
90%
Dissertations, extended projects and projects
10%
In-class activity
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
12
- Module leader
-
Dr Michel Tsamados
- Who to contact for more information
- m.tsamados@ucl.ac.uk
Intended teaching term:
Term 2 听听听
Postgraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
90%
Dissertations, extended projects and projects
10%
In-class activity
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
2
- Module leader
-
Dr Michel Tsamados
- Who to contact for more information
- m.tsamados@ucl.ac.uk
Intended teaching term:
Term 2 听听听
Undergraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
90%
Dissertations, extended projects and projects
10%
In-class activity
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
7
- Module leader
-
Dr Michel Tsamados
- Who to contact for more information
- m.tsamados@ucl.ac.uk
Last updated
This module description was last updated on 8th April 2024.
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