Høst 2023

BIO-8032 Advanced course on big data and AI for environmental, ecology and biology science - 5 stp

The course is administrated by

Institutt for arktisk og marin biologi

Type of course

PhD course for candidate interested in big data and AI in relation to the environment, ecology and biology sciences.

The course is available as a singular course.

Minimum number of students: 2

Course overlap

BIO-3032 Big data and Artificial intelligence for environmental, ecological and biological science: an introduction 1 ects

Course contents

This advanced course is designed to provide PhD students with a comprehensive understanding of the use of big data and artificial intelligence in the fields of environmental, ecological, and biological science. The course coversadvanced topics in data extraction, analytics, and predictive analysis, with a focus on the use of real big data sets relevant to these disciplines (e.g.data used by the students in their research). Students will learn to useGIS programsto analyse spatial big data and createmaps. Additionally, students will learn to design and implement artificial intelligence algorithms to analyse and evaluate big data. The course also delvesinto the ethical considerations surrounding the use of open-access data and the FAIR principles. By the end of the course, students will be well-equipped to apply their knowledge and skills to real-world problems at the intersection of science and society.

Admission requirements

Who can apply as a singular course student:

PhD student enrolled at another institution than UiT. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.
Holders of a master´s degree of five years or 3+2 years (or equivalent) may be admitted. These applicants must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English. To find out if this applies to you, see the following list: Proficiency in English must be documented - list of countries. For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document: Proficiency in english - PhD level studies

Objective of the course

Knowledge:

Have in-depth knowledge of the concepts and technologies of big data, and how they can be used to inform data-driven decision-making in environmental, ecological, and biological sciences
Have an advanced understanding of the ethical considerations surrounding the use of open-access data and the FAIR principles.
Have a thorough understanding of the ethical considerations and best practices for using AI in real-world applications

Skills:

Utilize advanced techniques for extracting, organizing, and analysing big data, including methods for testing, correlation, clustering, and data visualization
Have expertise in the use of GIS programs for spatial data analysis of big data
Design and implement artificial intelligence algorithms to analyse and evaluate big data

General competence:

Creativity in evaluating, visualizing, and analysing big data, and in writing artificial intelligence algorithms for analysing big data related to environmental, ecological, and biological sciences.
Apply different learned techniques to real-world data sets and interpret the results to inform data-driven decision making

Language of instruction

English

Teaching methods

The course workload is in total 150 hours. Several teaching methods are used in this course. These include lectures, seminars, datalabs and group projects.This is combined with reading, videos, quizzes, and writing an essay/report.