The Michigan Institute for Data Science (MIDAS) invites you to register for its workshop (fall, 2020) and bootcamp (summer, 2021) for biomedical scientists. 

Workshop on Introductory Data Science

Bootcamp on Introductory Data Science

Workshop objectives: This workshop will provide an introduction to data science from a biomedical perspective. After participating in this workshop trainees will be able to determine in which areas of clinical practice the application of data science, machine learning, artificial intelligence, or statistics is most appropriate, and where these techniques can be integrated into their own practice.

ACCME Statement: The University of Michigan Medical School is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The University of Michigan Medical School designates this live activity for a maximum of 5.5 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

When: Oct. 27, 2020, 9 am to 4 pm, virtual event.

Who should attend: this workshop is open to all biomedical scientists, but the content is geared towards junior faculty members who are interested in learning about the bootcamp and who plan to incorporate data science in their research.

What: The workshop includes an introduction to key concepts of data science, overview of the bootcamp and other training opportunities on campus, presentations by biomedical faculty members who have successfully incorporated data science in their research.

Lead instructor: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

For questions, please contact:Trisha Fountain, MIDAS Education Program Manager (tvfount@umich.edu) 

When: June 14th – June 18th, 2021 7am to 4:15pm

Who should attend: this workshop is open to all biomedical scientists, but the content is geared towards junior faculty members who plan to incorporate data science in their research.

  • Prerequisite: college level math and statistics.

What: the main components include:

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

Lead instructor: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

Other instructors:

  • Mathew Davis, Associate Professor in the Department of Systems, Populations and Leadership
  • Nambi Nallasamy, Assistant Professor of Ophthalmology
  • Jonathan Gryak, Research Assistant Scientist of Computational Medicine and Bioinformatics and Research Assistant Scientist at MIDAS
  • Michael Sjoding, Assistant Professor in the Division of Pulmonary and Critical Care and the Department of Internal Medicine
  • Michael Mathis, Assistant Professor of Anesthesiology

For questions, please contact: Trisha Fountain, MIDAS Education Program Manager (tvfount@umich.edu)

WORKSHOP ANCHOR

Workshop Schedule

09:00am – 09:15am Welcome & brief MIDAS presentation

Jing Liu, MIDAS Managing Director

09:15am – 09:30am Introduction of instructors and participants

Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

09:30am – 10:30am Goals and general overview of training program; who can best benefit from this training?

Speaker: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

10:30am – 10:45am Break

10:45am – 11:15am Why is training in data sciences necessary for physician-scientist in all fields?

Speaker: Brahmajee Nallamothu, Professor in the Division of Cardiovascular Diseases and the Department of Internal Medicine

11:15am – 12:00pm Data sciences, machine learning, artificial intelligence and statistics; similarities and differences

Speaker: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

12:00pm – 01:00pm Lunch break

01:00pm – 01:45pm Success story I – Data science for clinical diagnosis of ARDS

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

01:45pm – 02:30pm Success story II – Data science for anesthesiology

Speaker: Michael Mathis, Assistant Professor of Anesthesiology

02:30pm – 02:45pm Break

02:45pm – 04:00pm Description of boot camp, data science training resources on campus, Q&A and wrap-up

Moderator: Kayvan Najarian, Professor of Computational Medicine and Bioinformatics, and MIDAS Associate Director

04:00pm – 04:30pm Open Virtual Networking [Link to Networking Platform]

Hosted via Remo, introduction and instructions will be given by James Walsh (MIDAS Admin.)

BOOTCAMP ANCHOR

Bootcamp Schedule

Monday

7:00am – 8:30am Session 1: Welcome; introductions; review of the program and logistics; why data sciences, artificial intelligence and machine learning?

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

7:00am – 8:30am Session 6: Linear regression, logistic regression; ridge regression and Lasso regression

8:30am – 8:45am Break

8:45am – 10:15am Session 7: Naïve Bayes; feature selection and reduction

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: Python programming for linear regression, logistic regression; ridge regression and Naïve Bayes

Wednesday

7:00am – 8:30am Session 11: Artificial neural networks I

8:30am – 8:45am Break

8:45am – 10:15am Session 12: Regression tress

10:15am – 10:30am Break

10:30am – 12:00pm Session 13: Random Forest

12:00pm – 1:00pm Lunch Break

1:00pm – 2:30pm Session 14: Using machine learning for clinical and health applications II

2:30pm – 2:45pm Break

2:45pm – 4:15pm Session 15: Python programming for neural networks, regression trees and random forest

Thursday

7:00am – 8:30am Session 16: Support vector machines

8:30am – 8:45am Break

8:45am – 10:15am Session 17: Deep learning I

10:15am – 10:30am Break

10:30am – 12:00pm Session 18: Deep Learning II

12:00pm – 1:00pm Lunch Break

1:00pm – 2:30pm Session 19: Python programming for support vector machine and deep learning I

2:30pm – 2:45pm Break

2:45pm – 4:15pm Session 20: Python programming for support vector machine and deep learning II

Friday

7:00am – 8:30am Session 21: Strategies to add data science flavor to health related projects and grant proposals

8:30am – 8:45am Break

8:45am – 10:15am Session 22: Using machine learning for clinical and health applications III

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; Q&A; plans for follow-up sessions during the following year