Prerequisites: BIOE658J and BIOE651; or students who have taken courses with comparable content may contact the department.
This course will instruct students in the fundamentals of machine learning methods through examples in biological phenomenon and clinical data analysis. This course is designed to share knowledge of real-world data science and aid to learn complex machine learning theory, algorithms, and coding libraries in a simple way. The structure of this course is designed to walk students step-by-step into the world of machine learning. The course will cover major topics in Machine Learningsuch as supervised learning (i.e., regression, classification), unsupervised learning, deep learning, dimensionality reduction, and model selection and boosting. This course is packed with practical machine learning exercises that arebased on real-life examples. Studentswill learn machine learning theory, but they will also get hands-on practice building their models using programming tools such as Python and R.