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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