Academy Overview
Participants learned supervised and unsupervised machine learning as well as deep learning for clinical applications. They were able to determine which data science/artificial intelligence techniques are appropriate for a given clinical application and apply them to their own clinical and/or research activities. They also developed strategies for integrating data science into their grant applications, work effectively with data scientists, and build new collaborations.
Topics
- Key concepts of data science
- Introduction to Python programming and its use in data science
- Machine learning techniques, including support vector machines, artificial neural networks, and deep learning
- Examples of biomedical research projects that leverage data science
- Incorporating data science into biomedical grant proposals
Academy Details
- Certification of completion
- Ability to work on a breadth of data science topics with data science experts as collaborators
- Skills to abstractly consider data science solutions and apply them to biomedical problems
We will send payment instructions to accepted applicants.
- $3,000 for external participants (30% discount for U-M Alums)
- Thanks to support from the University, we are able to offer a reduced price of $100 for U-M personnel and students
Notifications to accepted registrants began on May 18.
- More than 14 days before the first day: full refund minus $50 processing fee
- Cancellation between 7 and 14 days of the first day: 50% refund
- Less than 7 days: no refund
This academy workshop is open to all U-M and external biomedical scientists, but the content is geared towards junior faculty members and those from the public and private sector who are interested in learning about incorporating data science into their research.
College-level math or statistics. No previous coding experience is required.
Students are expected to bring a laptop for programming components of the workshop.
Lead Instructor
Kayvan Najarian, PhD
Professor of Computational Medicine and Bioinformatics
MIDAS Associate Director
Other Instructors
Nambi Nallasamy
Assistant Professor of Ophthalmology
Michael Sjoding
Associate Professor,
Division of Pulmonary Critical Care
and Department of Internal Medicine
Ryan Stidham
Associate Professor,
Division of Gastroenterology
Departments of Medicine & Computational Medicine and Bioinformatics
Emily Wittrup
Research Manager,
Biomedical & Clinical Informatics Lab
Cristian Minoccheri
Postdoctoral Research Fellow
Ken Reid
Academy co-planner,
Data Scientist, MIDAS
Academy Schedule
7:00am – 8:30am : Session 1: Welcome and introduction to the program
8:30am – 8:45am : Break
8:45am – 10:15am : Session 2: Math foundations I – Brief introduction to mathematical foundations of machine learning
10:15am – 10:30am : Break
10:30am – 12:00pm : Session 3: Math foundations II – Brief introduction to mathematical foundations of machine learning
12:00pm – 1:00pm : Lunch Break
1:00pm – 2:30pm : Session 4: Clustering vs Classification; k-means; k-Nearest Neighbors
2:30pm – 2:45pm : Break
2:45pm – 4:15pm : Session 5: Introduction to Python programming
7:00am – 8:30am : Session 6: Simple Classification methods and feature analysis
8:30am – 8:45am : Break
8:45am – 10:15am : Session 7: Linear regression, logistic regression
10:15am – 10:30am : Break
10:30am – 12:00pm : Session 8: Model validation and assessment
12:00pm – 1:00pm : Lunch Break
1:00pm – 2:30pm : Session 9: Using machine learning for clinical and health applications I
2:30pm – 2:45pm : Break
2:45pm – 4:15pm : Session 10: Using machine learning for clinical and health applications II
7:00am – 8:30am : Session 11: Python programming for linear regression, logistic regression; ridge regression and Naïve Bayes
8:30am – 8:45am : Break
8:45am – 10:15am : Session 12: Artificial neural networks I
10:15am – 10:30am : Break
10:30am – 12:00pm : Session 13: Regression trees
12:00pm – 1:00pm : Lunch Break
1:00pm – 2:30pm : Session 14: Random Forest
2:30pm – 2:45pm : Break
2:45pm – 4:15pm : Session 15: Python programming for neural networks, regression trees and random forest
7:00am – 8:30am : Session 16: Support vector machines
8:30am – 8:45am : Break
8:45am – 10:15am : Session 17: Python programming for support vector machines
10:15am – 10:30am : Break
10:30am – 12:00pm : Session 18: Using machine learning for clinical and health applications III
12:00pm – 1:00pm : Lunch Break
1:00pm – 2:30pm : Session 19: Deep learning I
2:30pm – 2:45pm : Break
2:45pm – 4:15pm : Session 20: Deep Learning II
7:00am – 8:30am : Session 21: Python programming for deep learning
8:30am – 8:45am : Break
8:45am – 10:15am : Session 22: Guidelines on using machine learning for clinical applications
10:15am – 10:30am : Break
10:30am – 12:00pm : Session 23: Using machine learning for clinical and health applications IV
12:00pm – 1:00pm : Lunch Break
1:00pm – 3:00pm : Session 24: Strategies to add data science flavor to health related projects and grant proposals, and wrap up.
Follow-up Sessions
Follow-up sessions were offered to help attendees develop the data science components of their grant proposals and connect with collaborators.
ACCME Accreditation Designation
This activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of the University of Michigan Medical School and the Michigan Institute for Data & AI in Society. The University of Michigan Medical School is accredited by the ACCME to provide continuing medical education for physicians.
The University of Michigan Medical School designates this live activity for a maximum of XX AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Questions? Contact Us.
Contact Faculty Training Program Manager, Kelly Psilidis at psilidis@umich.edu