Teaching

MS650: Machine Learning for Biomedical Applications

Course director: Vijaya B. Kolachalama, PhD; Email: vkola(AT)bu.edu

This fall semester course is offered within the Division of Graduate Medical Sciences at BUSM.

Course description: Machine learning has given us self-driving cars, practical speech and face recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that we probably use it several times without even knowing it. It is an exciting way to make progress towards human-level artificial intelligence. In this day and age, it is critical that we expose BU students and trainees to this emerging area and educate them in this field. An introductory course such as this one is positioned to train the next generation of biomedical engineers and scientists to face data-driven challenges in the coming decades. The main feature of this course is that students will learn by doing! We will bring machine learning to life by showing fascinating use cases in biomedical sciences and tackling interesting real-world problems like disease risk assessment and predictive modeling. When students complete this introductory course, they will be in a position to analyze several types of biomedical datasets using machine learning techniques.

Pre-requisites: Basic familiarity with Microsoft Excel or similar analysis software. Prior experience in scientific programing languages (Matlab, Python, R, etc.) is a plus but not mandatory. Permission of the course director is required before joining the course. Please contact by email to setup an appointment before registering for the course.

Learning objectives:
• MS650 was carefully designed by the course director to teach the most effective machine learning techniques so that students gain practice implementing them and getting them to work for themselves. More importantly, students will gain the practical know-how needed to quickly and powerfully apply these techniques to new problems in biomedical sciences.
• Students will also get exposure to helpful tools, such as pre-written algorithms, codes and libraries, and to answer interesting questions.
• This course is aimed at machine learning literacy rather than proficiency. Therefore, the intent of the course director is to not expose students with tedious and rigorous mathematical definitions or equations, but make them comfortable with the tools so that they can tackle a new data challenge without getting overwhelmed with the jargon.