Applied Statistical and
Machine Learning
Course Description
The focus of this course will be on data science concepts including supervised methods and model assessment. Topics will include regularized regression (ridge, lasso, elastic net), tree-based methods (decision trees, random forests), ensemble learning (bagging, boosting, stacking), neural networks, cross-validation and bootstrapping.
Emphasis will be given to clinical research applications, with labs covering coding examples using the statistical software R. ​
Instructor
Zoe Guan, PhD
Assistant Investigator, Biostatistics, Massachusetts General Hospital
Assistant Professor of Medicine, Biostatistics, Harvard Medical School
Zoe Guan, PhD is an Assistant Investigator at Massachusetts General Hospital Biostatistics and Assistant Professor of Medicine at Harvard Medical School. She has given lectures on topics ranging from introductory biostatistics to advanced machine learning and genomics tools in educational programs such as the Bridge to Biostats Summer Program, Biostats Day Outreach Program, and Genomics Experience for Master's Students (GEMS) program at Memorial Sloan Kettering Cancer Center. She has also presented her research, which includes statistical and machine learning methods for cancer risk prediction, at national and international conferences.