Participants will learn supervised and unsupervised machine learning as well as deep learning for clinical applications. They will be 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 will also develop 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
Outcomes:
- 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
Tuition cost:
We will send payment instructions along with acceptance decisions.
- $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
Registration Timeline:
Notifications to accepted registrants began on May 18.
Cancellation Policy:
>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
Who should attend:
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.
Prerequisites:
College-level math or statistics. No previous coding experience is required.
Students are expected to bring a laptop for programming components of the workshop.
Location:
LSA Building – Room 1040 – 500 State St. Ann Arbor MI 48109
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
Academy co-planner,
Data Scientist, MIDAS
Tentative Academy Schedule
Monday, July 10
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
Tuesday, July 11
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
Wednesday, July 12
7:00am – 8:30pm
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
Thursday, July 13
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
Friday, July 14
7:00am – 8:30am
Session 21: Python programming for deep learning
8:30am – 8:45am
Break
8:45am – 10:15am
Session 22: Strategies to add data science flavor to health related projects and grant proposals
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 – 2:30pm
Session 24: Guidelines on using machine learning for clinical applications
2:30pm – 2:45pm
Break
2:45pm – 4:15pm
Session 25: Wrap-up
Follow-up Sessions
Follow-up sessions will be 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 Science. 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.
