Dear all,
Are you interested in mastering machine learning for biological data?
There are a few seats left for our online course "Introduction to Machine Learning with R" (16–20 February).
Course website:
Biology today generates complex multivariate data from multi-omics studies (genomics, proteomics, metabolomics, transcriptomics). Machine learning is a powerful tool to help analyze and interpret this data, alongside classical statistics.
In this course, you’ll get hands-on experience with multivariate methods and machine learning algorithms applied to biological datasets — including practical training using the tidyverse-friendly tidymodels framework.
Course programme (all sessions 2–7 pm Berlin time):
- ML basics and tidy ML with tidymodels
- Supervised learning: regression, classification, model selection
- Overfitting, resampling, model tuning
- Tree-based methods: Random Forest, Boosting
- Unsupervised learning: PCA, UMAP, Self-Organizing Maps
For the full list of our courses and workshops, please visit: https://www.physalia-courses.org/courses-workshops
Best regards,
Carlo
--------------------
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]
Are you interested in mastering machine learning for biological data?
There are a few seats left for our online course "Introduction to Machine Learning with R" (16–20 February).
Course website:
Biology today generates complex multivariate data from multi-omics studies (genomics, proteomics, metabolomics, transcriptomics). Machine learning is a powerful tool to help analyze and interpret this data, alongside classical statistics.
In this course, you’ll get hands-on experience with multivariate methods and machine learning algorithms applied to biological datasets — including practical training using the tidyverse-friendly tidymodels framework.
Course programme (all sessions 2–7 pm Berlin time):
- ML basics and tidy ML with tidymodels
- Supervised learning: regression, classification, model selection
- Overfitting, resampling, model tuning
- Tree-based methods: Random Forest, Boosting
- Unsupervised learning: PCA, UMAP, Self-Organizing Maps
For the full list of our courses and workshops, please visit: https://www.physalia-courses.org/courses-workshops
Best regards,
Carlo
--------------------
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]