🤖 Machine Learning for Research

📝 Course Overview

Machine learning is becoming increasingly important in life sciences research, enabling the analysis of complex datasets and the discovery of patterns that would be difficult to detect manually. This course provides an introductory, hands-on approach to machine learning concepts relevant to PhD researchers.

🚀 This course is part of the GS-LS R and python learning lines. It is designed for researchers who have coding experience but are new to machine learning and want to understand how to apply fundamental techniques to their own data.

🎯 Learning Objectives (Preliminary)

• Understand key machine learning concepts and terminology.
• Learn about supervised and unsupervised learning techniques.
• Gain an introduction to data preprocessing and feature selection.
• Explore basic model training, validation, and evaluation.
• Apply machine learning approaches to biological and research datasets.

🗓️ Course Schedule (To Be Determined)

This course is currently in development. The schedule will be finalised based on the needs of PhD researchers and the broader data science curriculum.

🎓 Final Assessment

The assessment structure is still being designed. It will likely involve a small hands-on project, where participants apply a machine learning model to a dataset relevant to their field of research.

🧰 Prerequisites

• Basic familiarity with Python or R programming (e.g., completion of Introduction to Python or Introduction to R).
• Some understanding of data analysis and statistics is helpful but not mandatory.

📚 Instructional Method (Proposed)

• Interactive lectures with step-by-step explanations.
• Hands-on coding exercises using Jupyter Notebooks and R markdown documents.
• Guided projects to apply machine learning techniques to real-world data.

🚀 How to Enrol

This course is currently in development. Stay tuned for more details!

For inquiries, contact me at l.w.dijkhuizen@uu.nl.

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Need more details? Feel free to reach out! 📩