2 ECTS Kourou Year 2 Semester 3 Compulsory Tool
Content
This course provides an introduction to supervised and unsupervised statistical learning techniques used in data analysis. In supervised learning, you will explore methods for regression and classification, such as K-Nearest Neighbors, where models are trained using labeled data to make predictions. In unsupervised learning, the course will cover techniques like clustering (K-means and HAC), dimensionality reduction (PCA) and methods for visualizing relationships between categorical data.
Learning outcomes
After completing the course, the student should be able to 1) set up of an appropriate experimentation, 2) design a sampling strategy to get a dataset that can then be used to answer a project questions, and 3) explore their data and reduce the dimension number of their data by using factorial analysis.
Teaching and learning methods
The course is entirely provided in the classroom (10h).
Type of assessment
- Group
- Written assignment
Students will complete a project in groups of two or three in which they will analyze a dataset to answer a scientific question in tropical ecology using the tools provided in this course.
Institution(s)
- Location: Kourou
- ECTS granting: AgroParisTech
- Organisation: AgroParisTech
Webpage(s)
Coordinator(s)
