HDS805 - Machine Learning in Healthcare
Course Description
This course covers the foundations of machine learning in healthcare systems including algorithms related to classification and regression prediction in supervised setting, clustering and dimension reduction in an unsupervised setting. Topics include data preprocessing and classification techniques such as logistic regression, support vector machines, KNN, Na'ive Bayes', ensemble methods such as random forests, boosted trees, XGBoost, dimension reduction techniques such as principal components analysis, t-distributed scholastic neighborhood embedding, ISOMAP, locally linear embedding, UMAP, multidimensional scaling.
Prerequisites:
Tuition & Fees
Continuing education course enrollment costs and fees vary depending on program criteria and student eligibility. For more information on continuing education course costs click here.
Location
Durham, NH
Credits
3.0 units