Introduction to Machine Learning in Remote Sensing
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| 155YUSU | Z,ZK | 5 | 2P+2C | Czech |
- Course guarantor:
- Tomáš Bayer, Martin Landa
- Lecturer:
- Tomáš Bayer, Martin Landa, Ondřej Pešek
- Tutor:
- Tomáš Bayer, Martin Landa, Ondřej Pešek
- Supervisor:
- Department of Geomatics
- Synopsis:
-
Machine learning is an integral part of data analysis and predictive modelling in many fields, including remote sensing. The aim of the course is to acquire basic knowledge of machine learning algorithms and principles of model generalization and practical design of process lines. In the course, students work independently on assigned examples of machine learning applications using remote sensing data. A prerequisite for the course is correct generalization of the trained model, including theoretical evaluation of overfitting and underfitting. In the projects, students create their own Python scripts and critically evaluate the results.
- Requirements:
-
Informatics 2 (Python) and basics of GIS and remote sensing
- Syllabus of lectures:
-
See Course Contents section
- Syllabus of tutorials:
-
See Course Contents section
- Study Objective:
-
The aim of the course is to acquire basic knowledge of practical application of machine and deep learning models in remote sensing and geoinformatics in general. It includes acquiring theoretical and practical knowledge of proper model generalization.
- Study materials:
- Note:
- Further information:
- https://geo.fsv.cvut.cz/gwiki/155YUSU
- Time-table for winter semester 2024/2025:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans:
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- Geodézie a kartografie, specializace Geomatika (compulsory elective course)