Description
Machine learning is rapidly emerging as a valuable tool in clinical practice, particularly in psychiatry, where it is transforming approaches to diagnosis, prognosis, and treatment decisions. By leveraging complex algorithms and large datasets, machine learning techniques enable researchers and clinicians to uncover patterns and insights that may not be apparent through traditional methods. This shift is particularly significant in psychiatric research, where variability in patient presentation and the complexity of mental health disorders pose unique challenges.
This course offers a comprehensive introduction to both fundamental and advanced concepts of machine learning, specifically tailored for applications in psychiatry. Participants will benefit from expert lectures delivered by leading external speakers, providing a well-rounded overview of the latest advancements in machine learning within psychiatry and related fields. The lectures emphasise the application of machine learning in psychiatric neuroimaging, showcasing its potential to enhance diagnostic accuracy, prognostic capabilities, and treatment decisions.
Through this course, you will gain insights into how machine learning can facilitate early detection of mental health disorders, predict treatment outcomes, and personalise interventions based on individual patient profiles. By bridging the gap between clinical practice and cutting-edge machine learning techniques, this course will equip you with the knowledge and skills needed to harness the power of machine learning in your research and clinical work.
This course is based on a selection of the resources from the Online Machine Learning Schools 2023 and 2024, organised by Nikolaos Koutsouleris, Germany, and his teams from LMU Klinikum Munich and King’s College London.
Learning objectives
- Understand core machine learning concepts and their applications: Grasp principles such as cross-validation, optimisation, and interpretability, as well as the clinical relevance of machine learning.
- Apply advanced machine learning techniques to real-world problems: Integrate strategies like multi-modal fusion, ensemble learning, and sequential prognosis to improve predictive performance and clinical utility.
- Promote fairness and ethical AI in machine learning: Recognise sources of bias, employ fairness metrics, and adopt methods to mitigate inequities in machine learning model.
Audience
From beginners to all levels of machine learning experience. No previous coding experience is required. The course is specifically designed for clinician scientists, PhD students, and early career researchers.
Course format
At the start of the course, your existing knowledge on the subject will be assessed through a pre-course test. The course comprises five 45-min lectures, which can be viewed at your convenience. Each lecture includes embedded knowledge tests within the recording and concludes with three additional knowledge tests.
The course is completed upon the successful completion of the post-course test.
Course schedule
Lecture 1 - Introduction to Machine Learning concepts and tools (Nikolaos Koutsouleris, Germany)
Lecture 2 - Machine Learning algorithms, optimization & multi-site data correction (Dom Dwyer)
Lecture 3 - Multi-modal data analysis & fairness in Machine Learning (Paris Lalousis, United Kingdom)
Lecture 4 - Transparent & interpretative Machine Learning (Ariane Wiegand, Germany)
Lecture 5 - Bridging machine learning and collaborative action research: a tale of engaging with diverse stakeholders in digital mental health (Munmun De Choudhury)
How to enrol
This course is freely accessible 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.
Registration is open now.
Should you have any questions, please feel free to contact us at courses@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
Munmun De Choudhury
Speaker
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
Munmun De Choudhury
Speaker