Machine Learning for Evolutionary Genomics (MLEG01) – Applications for Evolutionary Biology Only 4 places left! https://prstats.org/course/machine-learning-for-evolutionary-genomics-mleg01/?utm_source=chatgpt.com Delivered by Dr. Nikolay Oskolkov, a bioinformatician, computational biologist, and data scientist with extensive experience in machine learning, deep learning, population genomics, metagenomics, ancient DNA, dimensionality reduction, and computational biology. Learn how to apply machine learning and AI methods to evolutionary and genomic datasets using R and Python. Machine learning is increasingly important in evolutionary biology, providing powerful approaches for extracting patterns from large, complex, and high-dimensional genomic datasets. Applications range from population genomics and detecting introgression to ancient DNA, metagenomics, dimensionality reduction, sequence classification, and evolutionary inference. This course provides practical training in classical and modern machine-learning approaches, with hands-on examples drawn from evolutionary biology and genomics. What you'll gain A strong understanding of supervised and unsupervised machine learning Practical experience implementing machine-learning methods in R and Python Skills using PCA, t-SNE, and UMAP to investigate high-dimensional genomic data Experience applying machine learning to population and evolutionary genomics Understanding of machine-learning approaches for identifying introgressed genomic regions Applications to ancient DNA and ancient-status inference Experience with Random Forests and artificial neural networks Introduction to deep learning and convolutional neural networks Understanding of machine-learning applications in metagenomics and microbial evolution Experience treating DNA sequences as data for natural language processing (NLP) approaches Confidence selecting and evaluating machine-learning methods for evolutionary research Course format 5-day live, instructor-led online course 25 hours of training Hands-on practical exercises in R and Python Real-world biological and genomic datasets Strong focus on practical applications in evolutionary biology and genomics All sessions recorded Who is this course for? Evolutionary biologists Population and evolutionary geneticists Evolutionary genomic researchers Bioinformaticians and computational biologists Researchers working with ancient or environmental DNA Researchers analysing population-genomic and other high-dimensional biological datasets PhD students and early-career researchers interested in applying machine learning to evolutionary questions Participants should have a basic background in R or Python and some familiarity with biological datasets. Prior experience with machine learning or deep learning is not required. Why take this course? Evolutionary genomics increasingly involves datasets containing thousands or millions of genetic variables across individuals, populations, and species. Machine learning provides powerful methods for discovering structure within these high-dimensional datasets and tackling questions that can be difficult to address using conventional statistical approaches alone. The course has particularly strong applications to evolutionary biology through its coverage of population genomics, introgression, ancient DNA, genomic sequence analysis, dimensionality reduction, metagenomics, and evolutionary inference. Participants will explore how machine-learning methods can identify genomic patterns, classify biological sequences, investigate population history, and extract meaningful information from complex evolutionary datasets. Whether you're investigating population structure, introgression, adaptation, genomic variation, ancient DNA, microbial evolution, or other evolutionary genomic questions, this course provides a practical foundation for incorporating modern machine-learning approaches into your research. Course dates 14–18 September 2026 25 hours | Live online | £400 Learn more & enrol https://prstats.org/course/machine-learning-for-evolutionary-genomics-mleg01/?utm_source=chatgpt.com Questions? Email: oliver@prstats.org (to subscribe/unsubscribe the EvolDir send mail to evoldir@evoldir.net)