Machine Learning in Mental Health

This course consists of a series of self-paced video lessons designed to explore the theoretical concepts of machine learning, marking the beginning of a three-part workshop. By engaging with these lessons, you'll be prepared to actively participate in an engaging and interactive in-person workshop, hosted by DATAMIND and MQ Mental Health Research. During this workshop, you'll have the chance to apply your newfound knowledge, deepen your understanding, and enhance your learning journey.

What is machine learning in mental health?

Machine learning is a subfield of artificial intelligence (AI) and data science that focusses on algorithms able to automatically extract knowledge from data. When applied to mental health, machine learning promises to progress our understanding of the complex interactions between the many risk factors involved from multiple domains, including electronic health records, genetic data, and brain scans. Ultimately, machine learning could power clinical support tools for early prevention, diagnosis and treatment of mental health conditions. 

What will you learn?

This course covers various topics, including the use of machine learning in mental health epidemiology, essential mathematical concepts, data preparation, and a range of classification algorithms, such as decision trees and artificial neural networks. 

Who this course is for?

This course is designed for researchers, practitioners, and professionals interested in machine learning and mental health data analysis, catering to people with different levels of experience, including beginners. You do not need to have a strong background in statistics, computation, or mathematics to attend this course. It has been specifically created for those intending to attend the Machine Learning in Mental Health in-person workshop, part two, in our three-part series.

You’ll be learning from: 

Dr. Marcos Del Pozo Banos, a senior lecturer at Swansea University Medical School with 15 years of experience applying machine learning to a wide range of biometric problems in the fields of security, biology, and medicine. His research focuses on the analysis of routinely collected electronic health records for the study of suicide and self-harm prevention, applying advanced machine learning techniques within traditional epidemiological research.

Course Duration

3 hours and 45 minutes.

When Can You Start?

These materials are available for you to access at your convenience, allowing you to review them as often as needed before the in-person workshop.

Begin Your Learning

Just click play on the video below to begin. Utilise the playlist on YouTube to navigate through the course content at your own pace.

After completing this course, your next step will be to join us for the in-person workshop, followed by an online Q&A session.

  • The in-person session kicks off with a swift review of the self-paced material, ensuring everyone is on the same page.
  • Following the review, an interactive Q&A session will provide an opportunity for participants to seek clarification on specific topics.
  • Subsequently, we’ll delve into practical sessions constructing machine learning algorithms from scratch. This will make use of flow diagrams, pseudo-code, and your choice of R or python.
  • A hands-on analysis using a synthetic dataset will also be conducted, allowing participants to apply all the concepts learned.
  • The session concludes with a discussion on the best practices for applying machine learning in mental health research.
  • After the in-person event, participants will be invited to an online Q&A session.

This session serves as an additional opportunity to address any remaining or new queries, and provides a platform for further discussion.

Contact Us

Thank you for participating in this course. We value your opinion and would like to hear about your experience. Your feedback is essential in helping us enhance the course and create a better learning experience for future participants. 

To reach us directly, please contact us at We appreciate your input and look forward to making improvements based on your valuable insights. Thank you once again for being part of our learning community.

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