Advanced Methods and Health Applications in Machine Learning

1 Course Unit

Machine learning studies how computers learn from data and has enormous potential to impact biomedical research and applications. This course will cover fundamental topics in machine learning, with a focus on applications in biomedical informatics. Specifically, the course will cover: supervised learning methods including linear regression, logistic regression, nearest neighbors, support vector machines, decision trees, and random forests; unsupervised learning topics such as clustering and dimensionality reduction; neural networks and deep learning approaches for both supervised and unsupervised tasks, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders (AE), Generative Adversarial Networks (GAN), Graph Neural Networks (GNN), Transformers, Generative Pretrained Transformers (GPT), Large Language Models (LLM), and emerging areas in generative, trustworthy, and agentic AI; and the application of these machine learning techniques to a variety of biomedical informatics problems through the analysis of genomic, imaging, biomarker, electronic health record, clinical, and other biomedical data. Students are required to have completed a Python Class or have equivalent programming experience. It is recommended that students have basic knowledge in data analysis and biomedical research. Basic knowledge of machine learning, linear algebra, statistics and probability is preferred. NOTE: Non-majors need permission from the instructor. Fall 1 Course Unit