Course description
Machine learning and deep learning are a part of the field of Artificial Intelligence (AI), and their use with "Big Data" applications has grown exponentially over the last decade. Machine learning approaches are often applied to develop better models or to determine important variables within models, and are used currently for studies of climate, biology, geography, genetics, and many other fields relevant in the Earth and Environmental Sciences.
This course aims to give an overview of the background and status of AI, with focus on machine learning in general and deep learning in particular, for the Earth and Environmental sciences. Understanding some of the the commonly applied algorithms and considerations that should be taken when applying these (e.g., training data characteristics) is a goal of this course, and also to apply them partically in exercises.
Lectures and reading for the course will provide the student with material which will increase understanding of when and how to apply machine learning algorithms. Guest lectures will highlight ongoing applications of AI at different organisations. Students will participate in peer-based discussion groups during the course and write a final report relating the (potential) use of AI in their own PhD research.
Requirements and Selection
Entry requirements
Admitted to third cycle education.
Selection
- Doctoral students at the responsible Dept
- Doctoral students participating in the ClimBEco program (part of the joint GU/LU BECC Strategic Research Area)
- Doctoral students at the responsible Depts own faculty
- Doctoral students within another faculty at GU
- Doctoral students admitted to other higher education institutions
Other information
This University of Gothenburg PhD course is part of the BECC ClimBEco series together with Lund Univeristy. (https://www.cec.lu.se/climbeco/phd-courses)
Lectures and Exercises from 2-12 April require being in place at the University of Gothenburg.
Precedence for taking this course will be given to University of Gothenburg and ClimBEco PhD students.
A maximum of 30 students will be accepted.
Basic knowledge of Python is desired for the course. Students not having a Python background should learn basic Python before the course, through tutorials suggested by the course instructor.
Course syllabus
NGEO316
Department
Department of Earth Sciences
Subject
Natural Science and Mathematics
Keywords
AI, Artificial Intelligence, Earth Sciences, Environmental Sciences, Big Data