Machine Learning for Computational Biology.

Catalog description

Modern machine learning techniques are applied ubiquitously across the biomedical sciences to elucidate key insights from complex datasets. This course will introduce classical machine learning and fundamental deep learning techniques and how they can be applied for robust, reproducible, and interpretable analysis of biological data. Topics covered include, unsupervised learning, clustering, cross validation, bootstrapping, supervised learning via linear models, tree-based models, and deep learning models, and deep learning fundamentals and explainable machine learning techniques. All concepts will be taught in a practical way that involves implementing algorithms on cleaned biomedical datasets.