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By the end of the course, participants will be able to:

• Understand key machine learning concepts and their applications in life sciences and medical research
• Perform clustering to identify patterns in biological, ecological, and clinical data
• Apply classification techniques to predict outcomes and categorize data
• Use dimensionality reduction and feature selection to simplify complex datasets
• Evaluate machine learning models using appropriate validation techniques and metrics
• Address challenges related to bias, reproducibility, and interpretability in machine learning
• Apply machine learning techniques to their own research questions and datasets

The course runs from 09:30 to 15:00 daily, covering two main topics per session. Each topic consists of a lecture followed by hands-on exercises. The lectures will be interactive, incorporating coding challenges and discussions to encourage engagement and critical thinking. The hands-on exercises will use real-world datasets, allowing participants to apply their newly acquired skills directly.

Requirements

• Experience working with structured data in R or Python. This means you can:
  • Load data into data frames
  • Select from and manipulate data frames
  • Make exploratory plots from data
• No prior machine learning experience is required

Study load

• 5 days of instruction (09:30 – 15:00 each day)
• Additional self-study and practice are recommended for deeper understanding

Group size            

Maximum 20 participants, ensuring interactive and personalised learning.

Number of credits                                 

1.5 EC

Trainer

You will be trained by one of the professionals of , Utrecht ľ¹Ï¸£ÀûÓ°ÊÓ.

Course certificate                                

Participants who complete the course and submit a final project or presentation will receive a certificate of participation.

This course is free for GSLS PhD candidates. However: free of charge does not mean free of responsibility. Once you have signed up for a course, we expect you to attend. For every late cancellation or no-show we have had to disappoint others who would have liked to attend. This is our policy:

• You may cancel free of charge up to 4 weeks before the start of the course. After this date you can only cancel if you have a GSLS PhD candidate to replace you in the course. Send the name and contact information of your replacement to pcc@uu.nl, at least 2 working days before the start of the course;
• We expect that you actively attend the full course, but at least 80%. It is mandatory to attend the first session. If you are absent the first session you cannot follow the remaining of the course;
• Not meeting the above requirements means you will be charged a no-show fee (€ 75). We will send the invoice after the course has ended. We are unable to make any exceptions, unless you have a valid reason (i.e., illness or death in the family 1^st/2^nd degree or partner). Your supervisor has to send an e-mail to pcc@uu.nl indicating the reason.