Machine Learning for Evolutionary Genomics (MLEG01)
https://prstats.org/course/machine-learning-for-evolutionary-genomics-mleg01/
Delivered by experienced computational biologists, evolutionary geneticists, and machine learning researchers.
Learn how to apply Machine Learning (ML) and Artificial Intelligence (AI) methods to evolutionary and population genomic data.
The rapid growth of genomic datasets has created new opportunities and challenges for evolutionary biologists. Machine learning methods are increasingly being used to identify adaptive variation, detect signatures of selection, classify populations, predict evolutionary processes, and uncover complex patterns hidden within large genomic datasets. This hands-on course provides a practical introduction to machine learning workflows for evolutionary genomics using real-world examples and datasets. Machine learning is becoming a major tool in evolutionary genomics, particularly for population genetics, demographic inference, and selection detection. 
What you’ll gain
- A strong understanding of machine learning concepts in evolutionary biology
- Practical experience applying machine learning methods to genomic datasets
- Skills in predictive modelling, classification, and pattern recognition
- Understanding of supervised and unsupervised learning approaches
- Ability to evaluate model performance and avoid common pitfalls
- Confidence in interpreting machine learning outputs in an evolutionary context

Course format
- Live, instructor-led online training
- Hands-on coding with real-world evolutionary genomic datasets
- Interactive practical exercises throughout
- Strong focus on applied, research-ready workflows

Who is this course for?
- Evolutionary biologists and population geneticists
- Bioinformaticians and computational biologists
- Researchers working with genomic and sequencing datasets
- PhD students and quantitative life scientists
- Anyone interested in applying AI and machine learning to biological questions

Why take this course?
Modern genomic datasets often contain millions of genetic variants and increasingly complex biological signals. Traditional analytical methods can struggle to identify subtle patterns associated with adaptation, demographic history, and evolutionary processes.
Machine learning provides powerful tools for extracting information from these large datasets, enabling researchers to tackle questions involving population structure, natural selection, genomic prediction, and evolutionary inference. Recent advances have demonstrated the growing importance of machine learning and deep learning methods across population genetics, evolutionary genomics, and comparative biology. 
This course equips you with the practical skills needed to apply machine learning methods confidently to evolutionary genomic data, helping you generate new biological insights and build reproducible analytical workflows.

Learn more & enrol
PR Stats course page for Machine Learning for Evolutionary Genomics (MLEG01) https://prstats.org/course/machine-learning-for-evolutionary-genomics-mleg01/

Questions?
Email: oliver@prstats.org

Oliver Hooker
Managing Partner
Advanced Training for Researchers in the Life Sciences
oliver@prstats.org | www.prstats.org

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