Applied Machine Learning Using Health Data

3 units

This course teaches popular machine learning (ML) models using Python and their applications on health data. The topics include (1) Python programming basics (e.g., coding with Python, Python modules such as NumPy, Pandas, Matplotlib, and Scikit-learn); (2) Classification ML models; (3) Regression ML models; (4) ML model training and validation; (5) Support vector machines and decision trees; (6) Ensemble methods; (7) Dimensionality reduction; and (8) Unsupervised learning techniques. Students who complete this course will: (1) Understand the algorithms for ML models widely adopted in health sciences; (2) Proficiently apply ML models to analyze health data; and (3) Appraise the relative strengths and weaknesses of alternative ML models in the contexts of real-world problem-solving. Weekly assignment helps students deepen their understanding of critical concepts and sharpen their model building and problem-solving skills using health datasets. Students who complete this course will have a solid foundation to pursue more advanced artificial intelligence (AI) topics, including deep learning. A prerequisite for this course is an introductory course on statistics or biostatistics. No prior exposure to Python programming is needed.