Appreciating AI


Machine Learning (ML) and Artificial Intelligence (AI) methods have transformed multiple application domains in the past few years. The impact on computer vision and natural language processing are some of the most visible. However, their usefulness and impact in other sciences is only being explored now.  

In this two-day course, offered by the Data Science Laboratory, we will provide an overview of ML/AI methods with a focus on their applications to the sciences within the University of Copenhagen. We will also demonstrate a few concrete examples that can provide clearer impressions of the abilities of these methods. The main objective of this course is to expose teachers to ML/AI methods, so that they can co-create modules in their courses that use relevant ML/AI methodology for their domain. The integration of well-thought course modules will enable students to appreciate the strengths and weaknesses of ML/AI methods within their line of study. 
The course is primarily aimed at researchers and teachers at UCPH (incl. PhD students), who are engaged in some form of teaching at the university.  
After the two-day course, a subset of the participants can work with the course teachers to co-create domain specific ML/AI teaching modules.

When and where? 

  •  The course takes place on 10 August and 11 August 2023.  
  •  Both days are from 9.00 to 16.00 with lunch breaks at 12.00. 
  •  The course will be fully onsite. Location: Auditorium 10, HCØ, Universitetsparken 5 (North Campus)
  •  The course will include planned introduction lectures and interactive Python exercises using Jupyter notebooks.  

Who can participate? 

  • The course is intended for people with some interest in ML/AI methods. Hands on experience in ML/AI methods is an advantage but no prior knowledge is assumed.  
  • The course is open for researchers and teachers at University of Copenhagen. 
  • The number of participants is limited to 50.  

What is going to happen? 

Both days will comprise small lectures and interactive exercise sessions. The examples demonstrated during the course might be from SCIENCE/SUND.

Which topics? 

  • Introducing Machine Learning basics  
  • Introducing AI concepts (structured, unstructured, multi-modality data) 
  • Discussing AI caveats (ethics, CO2, bias, …)  
  • Demonstration of use-cases

How to register?  

Register by filling in this form 

Note: The course participation does not give ECTS.    

What to do before the course? 

We will be using the Python JupyterLab notebooks available via ERDA. Therefore, please make sure you have an account on ERDA.  

Further Questions?   

If you have questions, then you are most welcome to contact Raghavendra Selvan at the Data Science Lab (