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Webinar: Introduction to Machine Learning with Python

This course is intended for PhD students, post-docs and staff scientists who are interested in applying ML to analyze these data. Please note that this 2-day course will be streamed over 4 half-days, in the afternoon of the following dates: 27 May 2024; 3 June 2024; 10 June 2024; 17 June 2024

Registration deadline: 20 May 2024

More Info and Registration

General information

  • Time: 27 May 2024; 3 June 2024; 10 June 2024; 17 June 2024
  • Registration deadline: 20 May 2024
  • Location: Online
  • Fees:
    • Academic: 200 CHF
    • For-profit: 1000 CHF


With the rise of new technologies, the volume of omics data in biology and medicine has grown exponentially recently. A significant issue is to mine useful predictive knowledge from these data. Machine learning (ML) is a discipline in which computer algorithms perform automated learning by using data to assist humans in dealing with large volumes of multidimensional data. The analysis of such data is not trivial, and ML is a necessary tool to extract knowledge and make predictions that can advance the field of bioinformatics.

This 2-day course will introduce participants to common ML algorithms and how to apply them to omics data in extensive practical sessions. The practical sessions will be conducted in Python3 based on the widely applied scikit-learn ML framework. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML methods and processes, as well as the practical skills in applying them to real world problems using publicly available biological or medical data sets.

Learning outcomes

At the end of this course, participants are expected to:

  • Explain the ML taxonomy and the commonly used machine learning algorithms for analysing omics data
  • Describe differences between ML approaches and in which situations they can be applied
  • Critically evaluate applications of ML in omics studies
  • Implement common ML algorithms using the scikit-learn Python framework
  • Interpret and visualize the results obtained from ML analyses


No prior knowledge of ML concepts and methods is required. Knowledge of different omics data is recommended. Familiarity with the Python programming language and pandas data frames as well as a basic knowledge on statistics is required. This course will be streamed, you are thus required to have your own computer with an Internet connection. Additionally, you will need to have a recent python3 as well as a number of python libraries installed. Finally, although not mandatory, we also highly recommend you to use the same computer to connect to the zoom classroom and perform the exercises, otherwise we will have difficulties helping you debug your code.



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