Machine Learning for Evolutionary Genomics (MLEG01)

Course Description

Machine learning is rapidly transforming ecology, evolutionary biology, population genomics, and metagenomics by enabling researchers to extract patterns from increasingly large and complex datasets. This course provides a practical introduction to both classical and modern machine learning approaches, with a particular focus on biological applications. Participants will gain hands-on experience implementing machine learning algorithms in both R and Python while exploring real-world examples from population genomics, microbial ecology, metagenomics, ancient DNA, and microbiome research.

The course covers both supervised and unsupervised learning, dimensionality reduction techniques, deep learning, natural language processing approaches for DNA sequences, and challenges associated with high-dimensional biological data. Emphasis is placed on understanding the strengths, limitations, and biological interpretation of machine learning methods, enabling participants to critically evaluate and apply these approaches in their own research.

• Closest companions to MLEG01
• Deep Learning for Evolutionary Genomics (DLEG01)
• Deep Learning for Evolutionary Genomics: learn neural networks, predictive modelling, and AI methods for genomic data analysis.
• This is the natural follow-on course. While MLEG01 focuses on machine learning methods broadly, DLEG01 moves into neural networks and deep learning approaches for genomic data analysis. If you enjoy MLEG01, this would be the next course I’d take.
• Population Genomics and Structure Analysis (PGSA01)
• Learn population structure analysis for evolutionary biology using genetic data, population genetics methods, and evolutionary inference.
• Machine learning is often applied to questions about population structure, ancestry, admixture, and evolutionary history. PGSA01 provides the evolutionary genomics context that makes many ML applications meaningful.
• Genome-Wide Association Studies (GWAS) for Evolutionary Biology (GWAS01)
• GWAS for Evolutionary Biology: learn genome-wide association studies, quantitative trait analysis & genomic methods for evolutionary data.
• A very strong companion course. Many ML workflows in genomics are ultimately tackling prediction, trait associations, feature selection, and high-dimensional genetic data—exactly the kinds of problems encountered in GWAS.

• Strong supporting courses
• RNA-Seq Analysis (RNAA02)
• RNA-Seq analysis training – live online course covering experiment design, data QC, alignment, gene expression, DESeq2 differential expression, PCA, visualisation, and functional analysis.
• If you’re interested in applying machine learning to gene expression datasets, differential expression, or transcriptomics, this is one of the most relevant domain-specific courses. It covers DESeq2 workflows, PCA, visualisation, and functional analysis.
• Bayesian Modelling Using R-INLA Course (BMIN04)
• Learn Bayesian modelling with the R-INLA package. Build, fit, and interpret INLA models, define priors and latent effects, and apply INLA to real data in a five day course.
• Not genomics-specific, but highly relevant if you want a broader statistical modelling toolkit. Many researchers working in evolutionary genomics eventually combine ML methods with Bayesian inference and hierarchical models.
• Spatial Phylogenetics and the Bayesian Phylogenetic Mixed Model (PMM) (BPMM01)
• Learn spatial phylogenetics and Bayesian phylogenetic mixed models in R. Practical online training in phylogenetic modelling and comparative workflows.
• This is particularly relevant if your interests lie at the interface of genomics, phylogenetics, comparative biology, and evolutionary inference.

• Recorded courses
• A Comprehensive Introduction to Machine Learning (CIMLPR)
• Learn machine learning in R with this comprehensive course. Covers clustering, regression, trees, neural networks, and more—fully online and hands-on.
• Introduction to Machine Learning (IMLRPR)
• Learn machine learning in R with practical, hands-on instruction. Covers supervised, unsupervised models, interpretability, and evaluation in 40 hours.
• Machine Learning Intermediate to Advanced (MLIAPR)
• Advance your machine learning skills in R with deep learning, Bayesian methods, transformer models, clustering, and anomaly detection in this 28-hour live online course.
• Deep Learning using R (DLURPR)
• Learn deep learning in R using the torch ecosystem. Build MLPs, CNNs and transformer models through hands-on coding and gain practical skills for real research workflows.
• Machine Learning using Python (MLUPPR)
• Machine learning using Python: supervised and unsupervised learning, neural networks, and practical model building with scikit-learn and TensorFlow.
• Deep Learning Using Python (DLUPPR)
• Deep learning course using Python and PyTorch. Learn neural networks, CNNs and transformers through hands-on coding and real data.
• Machine Vision using Python (MVUPPR)
• Machine vision using Python: apply deep learning and computer vision with OpenCV and TensorFlow for real-world image classification and ecological data applications.
• Python for Data Science and Statistical Computing (PYDSPR)
• Learn Python for data science and statistical computing. Build skills in NumPy, Pandas and visualisation across two days of hands-on training for researchers and analysts.

What You’ll Learn