3.0

Average Course Rating

This course provides a rigorous introduction to the foundations of biomedical data science, emphasizing principled statistical modeling and machine learning with applications to biomedical and clinical data. Lectures focus on the theoretical and mathematical foundations underlying modern data science methods, while students develop practical proficiency through applied analysis of real biomedical datasets in Python. Topics include data wrangling, exploratory data analysis, and visualization; linear and regularized regression (feature weighting and selection, including LASSO); generative and discriminative classification (LDA/QDA, logistic regression); supervised learning (perceptron, support vector machines); non-linear methods (k-nearest neighbors, decision trees, random forests); and evaluation methodology (train/test splits, cross-validation, precision/recall and related metrics). The course also introduces dimensionality reduction and representation learning (PCA, t-SNE, UMAP), EM-based methods, and accessible deep learning fundamentals A dedicated topic on ethics, fairness, and responsible use of biomedical data is covered. Background in statistics and probability, linear algebra, and Python programming is useful.