Course program

What you need to know for the third edition of the course:

  • The next edition of the course will start 03-02-2023.
  • There are 2 tracks to choose from:
    • A full track that offers participants the ability to create their own AI solutions. In this track you will follow, besides the lectures, also practical sessions where you can pick up the skills needed to implement your own AI applications.
    • A light track that does not involve programming and offers participants a good understanding of what AI is and how it can benefit your work.

Full track program

  • The in depth course takes place on 16 Fridays.
  • The course days consist of lectures in the morning and afternoon Python practicals with cases focused on Radboudumc practice.
  • The program will mostly be held at Radboudumc in Nijmegen and a few times at JADS in Den Bosch.
  • The course includes project days in the Radboudumc where you will work in a team on a Radboudumc project.
  • We expect participants to spend on average 2-3 hours per week on homework and self study.
  • There will be an online training environment with support documentation, discussion forum and blended learning.
  • The course will end with a graduation ceremony where teams will present their project results and a certificate will be awarded.

An overview of the in-depth track schedule can be seen below.

Date Time Topic Content
03-02-2023 9-17h Introduction + Machine Learning 1 How is AI changing healthcare? How to run an AI project? Follow the CRIPS-DM model
for data science projects and start with understanding your data
10-02-2023 13-17h Machine Learning 1 Machine learning in Python
17-02-2023 9-17h Machine Learning 2 Learn the basic principles of machine Learning and
how to measure the Performance of machine learning models
03-03-2023 9-17h Machine Learning 3 A start is made to cover the most important supervised
machine learning algorithms and you will learn how to prepare your data
17-03-2023 9-17h Machine Learning 4 You will learn about unsupervised machine learning models
for data without any labels and learn how to Evaluate your models
24-03-2023 9-17h Deep Learning 1 Understand how convolutional neural networks can be used
for medical image analysis
31-03-2023 9-17h Deep Learning 2 Learn how to combine text, images and biomedical data for
better deep learning models
14-04-2023 9-17h Project Day 1 Defining project goals and understanding your data
21-04-2023 9-17h Data Engineering / AI Products The FAIR principles of data management
are covered and learn how to choose a suitable AI producte
28-04-2023 9-17h Project Day 2 Data preparation and model development
12-05-2023 9-17h Text Mining & Bioinformatics Learn how to get valuable insights from medical records
and about AI applications in genetics
26-05-2023 9-17h Project Day 3 Model optimization and result visualisation
02-06-2023 9-17h Ethics & Privacy / Deployment Ethical and privacy concerns regarding the use of AI are discussed and learn how to deploy your own AI product in practice
09-06-2023 9-17h Project Day 4 Evaluating model performance
16-06-2023 9-17h Project Day 5 Evaluating model performance
23-06-2023 9-13h Final Presentations Project groups will present their final results

Light track program

  • The short track takes takes 14 (half) Fridays.
  • The course consists of lectures with a focus on Radboudumc practice.
  • The program will mostly be held at Radboudumc in Nijmegen and a few times at JADS in Den Bosch.
  • For the projects you can bring in your own clinical case and you will be involved by providing guidance and feedback to a team.
  • We expect participants to spend on average 2-3 hours per week on homework and self study.
  • There will be an online training environment with support documentation, discussion forum and blended learning.
  • The course will end with a graduation ceremony where project teams will present their project results and a certificate will be awarded.

An overview of the short track schedule can be seen below.

Date Time Topic Content
03-02-2023 9-17h Introduction + Machine Learning 1 How is AI changing healthcare? How to run an AI project? Follow the CRIPS-DM model
for data science projects and start with understanding your data
17-02-2023 9-13h Machine Learning 2 Learn the basic principles of machine learning and
how to measure the Performance of machine learning models
24-03-2023 9-13h Deep Learning 1 Understand how convolutional neural networks can be used
for medical image analysis
31-03-2023 9-13h Deep Learning 2 Learn how to combine text, images and niomedical data for
better deep learning models
14-04-2023 9-17h Project Day 1 Defining project goals and understanding your data
21-04-2023 13-17h AI products Learn how to choose a suitable AI product
28-04-2023 9-17h Project Day 2 Data preparation and model development
12-05-2023 9-17h Text Miniing and Bioinformatics Learn how to get valuable insights from medical records
and about AI applications in genetics
26-05-2023 9-17h Project Day 3 Model optimization and result visualisation
02-06-2023 9-13h Ethics & Privacy Ethical and privacy concerns regarding the use of AI are discussed
09-06-2023 9-17h Project Day 4 Evaluating model performance
16-06-2023 9-17h Project Day 5 Evaluating model performance
23-06-2023 9-13h Final Presentations Project groups will present their final results