Open source teaching material and didactics (2023) - VVSOR - VVSOR

03 November 2023

Open source teaching material and didactics (2023)

We are happy to announce our meeting on

Integrating machine learning in statistics education: how and why?

Many academic degree programmes, such as psychology, medicine, economics and linguistics, offer courses on statistics. In these courses, classical and modern statistical methods are taught to provide students with knowledge and tools for analysing quantitative data. Also outside the realm of mathematics and computing, the use of machine learning and other AI techniques  for analysing quantitative data is on the rise.

During this symposium, we will discuss the question whether we should prepare the next generation of students with knowledge of all fields of data science or restrict the attention to statistical analysis. If we believe including machine learning in degree programmes in the social sciences, humanities and medical field is important we need to ask ourselves how we can most efficiently do so.

At the symposium, three speakers will share their ideas on this matter and there is ample time for discussion. Registration is free, also for non-members (although we obviously hope you’ll join the society as well as the event).

Date & time:

Friday, November 3rd 2023, 13:00-17:00

 

Location:

University of Groningen

Grote Kruisstraat 2/1

Groningen

Room: Kouwerzaal M.0161

Directions: https://www.rug.nl/staff/location/2212

 

Programme:

13.00 – 13.30     Welcome with coffee and tea

13.30 – 13.35     Casper Albers (University of Groningen) – Introduction

13.35 – 13.55     Maryam Amir Haeri (University of Twente) – “Teaching on the Bridge: Navigating from Statistics to Machine Learning and Back”

14.00 – 14.20     Gerton Lunter (University of Groningen) – “Teaching machine learning: what and to whom?”

14.25 – 15.00     Discussion

15.00 – 15.30     Coffee break

15.30 – 15.55     Said el Bouhadanni (Utrecht University) – “Teaching & (machine) learning; combining statistical thinking and machine learning education”

16.00 – 16.30     Discussion

16.30 – 17.15     Drinks

 

 

Frontpage image by: Mike MacKenzie