Introduction to Data Science and AI Summer Academy 2024

June 3, 8:30 AM - June 7, 2024, 4:30 PM

In-person at U-M Ann Arbor campus
Central Campus Classroom Building (CCCB), 1225 Geddes Ave, Ann Arbor, MI 48109

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Registration is now closed.

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.

Details

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

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

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.

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

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 Gutierrez Fernandez

Schmidt AI in Science Fellow,
Michigan Institute for Data & AI in Society

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 & AI in Society

Soumi Tribedi

Schmidt AI in Science Fellow,
Michigan Institute for Data & AI in Society

Anastasiia Visheratina

Schmidt AI in Science Fellow,
Michigan Institute for Data & AI in Society

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.

Schedule

*Please note room locations may vary*

Introduction

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

Light refreshments and lunch will be served in Room 1420.

8:00am-9:00am : Technical assistance will be available. Attendees should have the ability to run Jupyter notebooks locally on a laptop and install libraries, numpy, sci-kit-learn, pandas, PyTorch, matplotlib, and dependent libraries.

8:00am – 8:30am : Check-in with MIDAS staff outside of Rm. 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 (Rm. 1420)

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

Light refreshments and lunch will be served in Room 1420.

8:00am-9:00am : Technical assistance will be available. Attendees should have the ability to run Jupyter notebooks locally on a laptop and install libraries, numpy, sci-kit-learn, pandas, PyTorch, matplotlib, and dependent libraries.

8:00am – 8:30am : Check-in with MIDAS staff outside of Rm. 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 (Rm. 1420)

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

Light refreshments and lunch will be served in Room 1420.

8:00am-9:00am : Technical assistance will be available. Attendees should have the ability to run Jupyter notebooks locally on a laptop and install libraries, numpy, sci-kit-learn, pandas, PyTorch, matplotlib, and dependent libraries.

8:00am – 8:30am : Check-in with MIDAS staff outside of Rm. 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 (Rm. 1420)

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

Light refreshments and lunch will be served in Room 1420.

8:00am – 8:30am : Check-in with MIDAS staff outside of Rm. 1420.

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 (Rm. 1420)

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

8:00am – 8:30am : Check in with MIDAS staff outside Room 1420.

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 (Rm. 1420)

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

8:00am – 8:30am : Check in with MIDAS Staff outside of Room 1420.

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 (Rm. 1420)

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

8:00am – 8:30am : Check-in with MIDAS staff outside of Rm. 1420.

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 (Rm. 1420)

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 Fernánde
Location: Central Campus Classroom Building (CCCB) Room 3460

8:30am – 10:00am : Session 1: Advancing Computer Vision in Electron Microscopy through Deep Learning Techniques

8:00am – 8:30am : Check in with MIDAS staff outside Room 1420. 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 (Room 1420)

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

8:00am – 8:30am : Check in with MIDAS staff outside Room 1420.

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 (Room 1420)

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 Us.

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