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Academy Overview

The Introduction to Data Science and AI Summer Academy introduces the basics of data science and AI methods to researchers (especially faculty). Our goal is to lower the barrier of entry for domain research scientists who plan to adopt data science and AI methods, and enable them to collaborate with data scientists and AI experts more effectively. 

Students are expected to bring a laptop for programming components of the workshop.

Academy Details

Outcomes

  • Skills to incorporate data science and AI methods in your research
  • Effective collaboration with data science and AI experts
  • Certification of completion

Tuition Cost

Tuition to attend: $100 for U-M internal | $3,000 for external participants (30% discount for U-M Alums)

Cancellation Policy:

  • 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

Who Should Attend?

This academy workshop is open to U-M researchers and trainees, and those from the public and private sector who are interested in learning about incorporating data science and AI into their research. Faculty members are particularly encouraged to attend.

Prerequisites

  • College-level math or statistics.
  • Some coding experience is recommended, but not required.

Location

Central Campus Classroom Building (CCCB)
1225 Geddes Ave, Ann Arbor, MI 48109

Parking available nearby includes a parking structure for U-M Blue/Gold permit holders, located at 525 Church St., and metered street parking along Church St. There is also a public garage at 650 S. Forest Ave. View available public parking in Ann Arbor here and real time occupancy counts and public parking structures here.

Instructors

Yang Chen

Assistant Professor of Statistics,
College of Literature, Science, and the Arts;
Research Assistant Professor,
MIDAS

Paramveer Dhillon

Assistant Professor of Information,
School of Information

Vital Fernández

Vital Gutierrez Fernandez

Schmidt AI in Science Fellow,
Michigan Institute for Data Science

Xun Huan

Assistant Professor of Mechanical Engineering,
College of Engineering

Kerby Shedden

Professor of Statistics and Biostatistics; Director, Center for Statistical Consultation and Research

Nanta Sophonrat

Schmidt AI in Science Fellow,
Michigan Institute for Data Science

Soumi Tribedi

Soumi Tribedi

Schmidt AI in Science Fellow,
Michigan Institute for Data Science

Anastasiia Visheratina

Schmidt AI in Science Fellow,
Michigan Institute for Data Science

Some instructors of this academy are the postdoctoral scholars in the Eric and Wendy Schmidt AI in Science postdoctoral program and the Michigan Data Science Fellows program.

Academy Schedule

*Please note room locations may vary*

Introduction

Instructor: Kerby Shedden
Location: Central Campus Classroom Building (CCCB) Room 1420


8:30am – 10:30am

Math and statistics review – foundations for ML


10:30am – 10:45am

Break


10:45am – 12:15pm

Math and statistics review – foundations for ML (continued)


12:15pm – 1:15pm

Lunch Break


1:15pm – 2:45pm

Basic regression analysis in Python


2:45pm – 3:00pm

Break


3:00pm – 4:30pm

Basic PCA and dimension reduction in Python

Introduction to Machine Learning

Instructor: Paramveer Dhillon
Location: Central Campus Classroom Building (CCCB) Room 1420


8:30am – 10:30am

Session 1 – Introduction to Machine Learning and Data Preprocessing


10:30am – 10:45am

Break


10:45am – 12:15pm

Session 2 – Supervised Learning: Classification Algorithms


12:15pm – 1:15pm

Lunch Break


1:15pm – 2:45pm

Session 3 – Supervised Learning: Regression Algorithms


2:45pm – 3:00pm

Break


3:00pm – 4:30pm

Session 4 – Unsupervised Learning: Clustering and Dimensionality Reduction

Introduction to Deep Learning

Instructor: Paramveer Dhillon
Location: Central Campus Classroom Building (CCCB) Room 1420


8:30am – 10:30am

Session 1 – Introduction to Deep Learning


10:30am – 10:45am

Break


10:45am – 12:15pm

Session 2 – Convolutional Neural Networks (CNNs)


12:15pm – 1:15pm

Lunch Break


1:15pm – 2:45pm

Session 3 – Recurrent Neural Networks (RNNs)


2:45pm – 3:00pm

Break


3:00pm – 4:30pm

Session 4 – Introduction to Transformers

Track 1: Experimental Design for Optimal Data Collection and Model Building

Instructor: Xun Huan
Location: Central Campus Classroom Building (CCCB) Room 0460


8:30am – 10:00am

Introduction and non-model-based experimental design


10:00am – 10:15am

Break


10:15am – 12:00pm

Design criteria


12:00pm – 1:00pm

Lunch Break


1:00pm – 2:30pm

Numerical approximations of design criteria


2:30pm – 2:45pm

Break


2:45pm – 4:30pm

Hands-on exercise: nested Monte Carlo for nonlinear experimental design

Track 2: Advanced AI methods for physical sciences

Instructors: Soumi Tribedi, Nanta Sophonrat
Location: Central Campus Classroom Building (CCCB) Room 3460


8:30am – 10:00am

Session 1: Molecular property prediction using graph neural networks.

Presented by Soumi Tribedi


10:00am – 10:15am

Break


10:15am – 11:45am

Session 2: Hands-on exercise: Building a GNN for molecular property prediction using PyTorch.

Presented by Soumi Tribedi


11:45am – 12:15pm

Moderated Discussion


12:15pm – 1:15pm

Lunch Break


1:15am – 2:45pm

Session 3: Introduction to application of machine learning in chemical reaction prediction

Presented by Nanta Sophonrat


2:45pm – 3:00pm

Break


3:00pm – 4:30pm

Session 4: Hands-on session: predict a synthesis route for a desired product using reaction templates

Presented by Nanta Sophonrat

Track 3: Bayesian inference

Instructor: Yang Chen
Location: Central Campus Classroom Building (CCCB) Room 0420


8:30am – 10:00am

• Review of Probability Theory
• Monte Carlo Methods
• Basics of Bayesian Modeling and Inference


10:00am – 10:15am

Break


10:15am – 12:15pm

(continued)

• Review of Probability Theory
• Monte Carlo Methods
• Basics of Bayesian Modeling and Inference


12:15pm – 1:15pm

Lunch Break


1:15pm – 2:45pm

Single and Multi-Parameter Models


2:45pm – 3:00pm

Break

3:00pm – 4:30pm

(continued) Single and Multi-Parameter Models


Track 1: Experimental Design for Optimal Data Collection and Model Building

Instructor: Xun Huan
Location: Central Campus Classroom Building (CCCB) Room 3420


8:30am – 10:00am

Design optimization methods


10:00am – 10:15am

Break


10:15am – 12:00pm

Sequential optimal experimental design


12:00pm – 1:00pm

Lunch Break


1:00pm – 2:30pm

Sequential optimal experimental design (continued)


2:30pm – 2:45pm

Break


2:45pm – 4:30pm

Hands-on exercise: policy gradient-based sequential experimental design

Track 2: Advanced AI methods for physical sciences

Instructors: Anastasia Visheratina, Vital Fernández
Location: Central Campus Classroom Building (CCCB) Room 3460


8:30am – 10:00am

Session 1: Advancing Computer Vision in Electron Microscopy through Deep Learning Techniques

Presented by Anastasia Visheratina


10:00am – 10:15am

Break


10:15am – 11:45am

Session 2: Hands-on exercise: Deep Learning-Based Segmentation of Particles in Electron Microscopy Images

Presented by: Anastasia Visheratina


11:45am – 12:15pm

Moderated Discussion


12:15pm – 1:15pm

Lunch Break


1:15am – 2:45pm

Session 3: Introduction to astronomical spectra and 
Bayesian inference to solve a system of equations.

Presented by: Vital Fernández


2:45pm – 3:00pm

Break


3:00pm – 4:30pm

Session 4: Hands on on probabilistic programming libraries.

Presented by: Vital Fernández

Track 3: Bayesian inference

Instructor: Yang Chen
Location: Central Campus Classroom Building (CCCB) Room 0420


8:30am – 10:00am

 Bayesian Computational Methods


10:00am – 10:15am

Break


10:15am – 12:15pm

 Bayesian Computational Methods


12:15pm – 1:15pm

Lunch Break


1:15pm – 2:45pm

Bayesian Inference in Practice: case studies


2:45pm – 3:00pm

Break


3:00pm – 4:30pm

(continued) Bayesian Inference in Practice: case studies

Questions?

Contact Faculty Training Program Manager, Kelly Psilidis at psilidis@umich.edu