Course: Data Science 5 credits: 5

Course code
BFVM23DATASCNC5
Name
Data Science 5
Study year
2023-2024
ECTS credits
5
Language
English
Coordinator
F. Feenstra
Modes of delivery
  • Lecture
Assessments
  • Machine Learning - Assignment

Learning outcomes

Student:  

  1. You utilize an argumentative approach to select and apply appropriate unsupervised machine learning techniques and algorithms for a given problem in life science, while also being able to evaluate their effectiveness. 

  1. You can execute the general steps of the machine learning lifecycle, including data engineering, model selection, hyperparameter tuning, and model deployment, and apply them effectively to life science problems. 

  1. You demonstrate knowledge and understanding of the challenges and limitations of unsupervised machine learning in the context of the life science problem at hand 

Content

This course is an introduction to machine learning, with a particular focus on unsupervised machine learning techniques in the domain of life science.  

Throughout this course, students will learn about the basic concepts and techniques of unsupervised machine learning, and how to implement these including data reduction, multidimensional scaling, manifold learning, clustering, and outlier detection. They will also be introduced to some of the most widely used algorithms in these areas and how these techniques can be applied to problems in life science. 

Furthermore, this course will cover general steps in the machine learning lifecycle, including data engineering, model selection, hyperparameter tuning, and model deployment. 

By the end of this course, students will have a solid understanding of the principles and applications of machine learning in the context of life science and be well-prepared to take on more advanced topics in machine learning.  

Included in programme(s)

School(s)

  • Institute for Life Science & Technology
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