Objectives of Programme's Learning Outcomes

• In the field of biological sciences and particularly in the field of the biology of organisms and ecology, possess highly specialised and integrated knowledge and a wide range of skills adding to those covered in the Bachelor's programme in biological sciences.
• Conduct extensive research and development projects related to biological sciences, in the biology of organisms and ecology.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Contributing to a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
• Show initiative and be able to work independently and in teams.
• Manage and lead research, development and innovation projects.
• Understand unprecedented problems in biological sciences, and more specifically in the biology of organisms, ecology and its applications.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Develop and integrate a high degree of autonomy.
• Pursue further training and develop new skills independently.
• Apply scientific methodology.
• Critically reflect on the impact of their discipline in general, and on the contribution to projects.

Learning Outcomes of UE

At the end of this course, the sutdents will be able to classify main plankton groups on the basis of morphological criteria, and in the same time, they will be able to apply image analysis and machine learning techniques on a computer to classify this plankton (semi-)automatically. Thus, tey will have learned to apply advanced statistical techniques to solve a biological problem.

Content of UE

This course focuses on the study of plankton organisms with computers. It applies automation of the analysis and classification of plankton images.

Prior Experience

Use of the software R and RStudio. Bases in machine learning, including LDA, SVM and random forest, cross-validation and analysis of confusion matrices.

Type of Assessment for UE in Q2

• Presentation and/or works

Q2 UE Assessment Comments

A report containing the description of the process leading to a trianing set, a classifier et the study of its performances to classify plankton, together with notes about morphological criteria used to establish the plankton groups to be discriminated.

Type of Assessment for UE in Q3

• Presentation and/or works

Q3 UE Assessment Comments

Similar to Q2.