Registration for this workshop is now closed

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

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 sent 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

Dr. Najarian

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

Ken Reid

Academy co-planner,
Data Scientist, MIDAS

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: 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 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.

Create a MiCME Account to Claim CME Credit