Description
The Online Machine Learning School for Precision Medicine provides comprehensive training on machine learning (ML) methodologies for beginners, tailored specifically to clinical research applications in psychiatry and neuroscience. This fully online course will take place from 3 to 7 November 2025, running daily from 09:30 to 17:00 CET.
The programme is designed to enhance participants' understanding of ML concepts through practical sessions and theoretical discussions. The course covers fundamental ML topics, including nested cross-validation, strategies to mitigate overfitting, external validation processes, and interpretability through explainable AI (XAI). Participants will also explore the TRIPOD-AI guidelines to enhance the reporting quality and transparency of ML research findings. Additionally, specific attention will be devoted to challenges frequently encountered in psychiatric research, such as site-correction and data fusion techniques. The curriculum incorporates perspectives from guest speakers who will discuss ethical, practical, and regulatory issues relevant to integrating ML into clinical practice.
Two participation tracks are available to accommodate different learning needs:
In-depth track: Includes access to seminars, expert lectures and hands-on tutorials and interactive workshops using NeuroMiner, a user-friendly ML toolbox developed for clinical neuroscience and neuroimaging research.
Basic track: Includes access to seminars and expert lectures only.
No prior coding knowledge is required, enabling broad accessibility to clinicians and researchers seeking to effectively incorporate ML into their work. The course is applicable to participants working with a wide variety of data including clinical, neuroimaging and genetic data.
This course is organised by Prof. Nikolaos Koutsouleris and collaborators from LMU Munich, King’s College London, and the ECNP Neuroimaging Network.
Learning objectives
- Understand the fundamentals of ML and their relevance to precision medicine.
- Apply ML techniques using NeuroMiner to analyse clinical and neuroimaging data.
- Evaluate ML models with a focus on generalisability, ethical considerations, and adherence to reporting standards.
Audience
Clinician scientists, PhD students, Early Career Scientists and professionals in psychiatry, neuroscience, and related fields interested in ML applications.
Course format
This course will be delivered online from 3 to 7 November 2025, from 09.30 to 17.00 CET. There are two tracks available to best fit your needs.
The in-depth track (Fee: 260 EUR ex VAT)
- Four seminars
- Four expert lectures
- Hands-on tutorials: practical sessions with expert tutors on utilizing NeuroMiner for ML pipeline implementation.
- Interactive workshops: collaborative exercises focusing on model evaluation and ethical considerations.
To successfully complete the in-depth track, participants must attend all sessions and actively participate in the tutorials and workshops.
The basic track (free of charge):
- Four seminars
- Four expert lectures
- To successfully complete the basic track, participants must attend all sessions.
Course schedule
The in-depth track covers all elements (bold and not bold).
The basic track covers the elements not in bold.
Day 1: 3 November 2025
09:30-10:15 - Introduction to Machine Learning Concepts and Tools Seminar (Nikolaos Koutsouleris, Germany)
10:15-10:30 - Discussion
10:30-11:00 - General information about the tutorials and the Kaggle competition
11:00-12:00 - Group tutorial: Introduction to NeuroMiner, cross-validation and preprocessing
12:00-13:00 - Break
13:00-15:00 - Tutorial continues at own pace using Kaggle competition data
15:00-15:45 - Group tutorial: Wrap up and question time
16:00-17:00 - Guest speaker 1: Sophia Frangou, United States
Day 2: 4 November 2025
09:30-10:15 - Machine Learning Algorithms, Optimization & Multi-site Data Correction Seminar (Dom Dwyer, Austrailia, or Fiona Coutts, United Kingdom)
10:15-10:30 - Discussion
10:30-12:00 - Group tutorial: Algorithms, optimization and multi-site correction
12:00-13:00 - Break
13:00-15:00 - Tutorial continues at own pace using Kaggle competition data
15:00-15:45 - Group tutorial: Wrap up and question time
16:00-17:00 - Guest speaker 2: Raquel Iniesta, United Kingdom, or Daniel Rückert, United Kingdom
Day 3: 5 November 2025
09:30-10:15 - Multi-modal Data Analysis & Fairness in Machine Learning Seminar (Paris Lalousis, United Kingdom)
10:15-10:30 - Discussion
10:30-12:00 - Group tutorial: Feature selection and model application
12:00-13:00 - Break
13:00-15:00 - Tutorial continues at own pace using Kaggle competition data
15:00-15:45 - Group tutorial: Wrap up and question time
16:00-17:00 - Guest speaker 3: Raquel Iniesta, United Kingdom, or Daniel Rückert, United Kingdom
Day 4: 6 November 2025
09:30-10:15 - Transparent and Interpretable Machine Learning (Ariane Wiegand, Germany)
10:15-10:30 - Discussion
10:30-11:30 - FAQ with the NM developer – Nikolaos Koutsouleris, Germany
11:30-12:00 - Group tutorial: Interpretable machine learning
12:00-13:00 - Break
13:00-15:00 - Tutorial continues at own pace using Kaggle competition data
15:00-15:45 - Group tutorial: Wrap up and question time, reminder of Kaggle competition
16:00-17:00 - Guest speaker 4: Katharina Schultebraucks, United States
Day 5: 7 November 2025
09:00-12:00 - Kaggle competition continues with access to tutors
12:00-12:15 - Submission of final ML results to Kaggle
12:15-14:00 - Winners prepare short slides outlining their strategy
14:00-15:00 - Presentation of Kaggle competition results, winners present strategy, and wrap-up of the machine learning school
How to enrol
Application to this course is available to all individuals with a myECNP account. If you do not yet have a myECNP account, please visit the ECNP website at ecnp.eu and navigate to the ‘myECNP’ tab.
- Application deadline: 27 October 2025
- Notification of acceptance: Upon payment (in-depth track) or registration (basic track)
Should you have any questions, please feel free to contact us at knowledge-hub@ecnp.eu.
Faculty
Nikolaos Koutsouleris
Course organiser/Speaker
University of Munchen | Germany
Dom Dwyer
Speaker
Paris Lalousis
Speaker
King's College London | United Kingdom
Ariane Wiegand
Speaker
Max Planck Institute of Psychiatry | Germany
Sophia Frangou
Speaker
Icahn School of Medicine at Mount Sinai | United States
Raquel Iniesta
Speaker
King's College London | United Kingdom
Daniel Rückert
Speaker
Imperial College London | United Kingdom
Katharina Schultebraucks
Speaker
NYU Langone Health | United States
Faculty
Nikolaos Koutsouleris
Course organiser/Speaker
University of Munchen | Germany
Dom Dwyer
Speaker
Paris Lalousis
Speaker
King's College London | United Kingdom
Ariane Wiegand
Speaker
Max Planck Institute of Psychiatry | Germany
Sophia Frangou
Speaker
Icahn School of Medicine at Mount Sinai | United States
Raquel Iniesta
Speaker
King's College London | United Kingdom
Daniel Rückert
Speaker
Imperial College London | United Kingdom
Katharina Schultebraucks
Speaker
NYU Langone Health | United States