Description and Expected Results

Participants will learn how to a) utilize supervised and unsupervised machine learning for clinical applications; b) employ deep learning for clinical applications; c) determine which data science/artificial intelligence techniques are appropriate for a given clinical application; and d) implement these methodologies in Python code.

After completing the bootcamp, participants 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.

View Schedule

What: the main components include:

  • Math and algorithmic foundations for data science
  • Key concepts of data science
  • Introduction to Python programming
  • Machine learning, including support vector machines, artificial neural networks, and deep learning
  • Example of biomedical research projects with data science
  • Incorporating data science in biomedical grant proposals

Tuition cost: We will send payment instructions along with acceptance decisions

$100 for U-M personnel
$3,000 for external participants (30% discount for U-M Alums)

Cancellation Policy:

>14 days before the first day of camp: full refund minus $50 processing fee
Cancellation between 7 and 14 days of the first day of camp: 50% refund
Less than 7 days: no refund

Who should attend:

This bootcamp 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. Please register by June 25. Later registrants will be accepted only if spots are available. 

Prerequisites: College-level math and statistics

Location: Weiser Hall 10th floor – 500 Church St. Ann Arbor, MI 48109

Registration Closed

Joint Providership ACCME Statement:

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 (MIDAS). 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.

Lead instructor:

Dr. Najarian

Kayvan Najarian
Professor of Computational Medicine and Bioinformatics
MIDAS Associate Director

Other Instructors:

Ivo Dinov
Professor of Human Behavior and Biological Sciences

Jonathan Gryak
Research Assistant Scientist of Computational Medicine Bioinformatics, Senior Scientist at MIDAS

Michael Mathis
Assistant Professor of Anesthesiology

Nambi Nallasamy
Assistant Professor of Ophthalmology

Michael Sjoding
Assistant Professor in the Division of Pulmonary
Critical Care and the Department of Internal Medicine

Ryan Stidham
Assistant Professor of Gastroenterology

Bootcamp Schedule

Monday, July 18

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 19

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 20

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 21

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 22

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

Two Half-Day Follow-up Sessions

These follow-up half-day workshops will be scheduled for the subsequent two terms after the bootcamp. These workshops will reinforce the knowledge and experiences of the previous bootcamp, while providing additional guidance in applying data science skills to problems in biomedical science and developing these results into grant applications. In these workshops, trainees will have opportunities to:

  • present and discuss their current work with other trainees, providing peer-to-peer support in solving common challenges and sharing best practices; and
  • receive guidance from MIDAS trainers in problem solving and further developing their research ideas for applications of data science to biomedical science.