Current training events
Fall Semester 2024
- Practical Machine Learning Seminar Series – Priyam Mazumdar
Hackathons
CAII puts on regular hackathons to give students, faculty and staff across the University of Illinois at Urbana-Champaign a platform to showcase their skills through friendly competition. These hackathons are 2-day intensive experiences where teams work on challenging problems faced by academia and industry that involve deep learning.
The goal of these events is to create a functional model that can be picked up for further development by a related research group. Top performing teams are awarded with cutting-edge hardware for AI research and gaming. Past hackathons were co-sponsored by NVIDIA and the Steven and Beth Ashby Graduate Support Fund.
Past hackathon events:
- 2024 Ashby Prize in Computational Science Hackathon – April 20-21, 2024
- Ashby Prize in Computational Science Hackathon— April 18 – 25, 2022
- NCSA-NVIDIA AI Hackathon III — March 7-8, 2020
- NCSA-NVIDIA AI Hackathon II — November 9-10, 2019
- NCSA-NVIDIA AI Hackathon I — October 5-6, 2019
Training
AI knowledge is in high demand both for academic research and for job opportunities in the private sector. To help encourage increased exposure in this area, CAII conducts training sessions that are open to anyone interested in learning more about AI.
Our goal is to give everyone the opportunity to access AI knowledge and use cutting-edge technologies available at NCSA and the University of Illinois.
If learning about AI appeals to you, attend one of our training sessions.
Current training events
Fall Semester 2024
- Practical Machine Learning Seminar Series – Priyam Mazumdar
Archived training materials
Fall Semester 2022
- Getting Started with HAL – Volodymyr Kindratenko
- Introduction to Machine Learning and Neural Networks – Aaron Saxton
- Introduction to TensorFlow – Shirui Luo
- Introduction to PyTorch – Priyam Mazumdar
- Data Loading and Tools in PyTorch and TensorFlow – William Eustis
Spring 2022
- Getting Started with HAL – Dawei Mu
- Introduction to Machine Learning and Neural Networks – Asad Khan
- Introduction to TensorFlow – Asad Khan
- Introduction to PyTorch – Joshua Yao-Yu Lin
- Machine Learning First Deep Neural Network – Kristopher Keipert, NVIDIA
- Convolution Neural Network Models – Jeff Layton, NVIDIA
- Machine Learning with H2O on HAL – Dawei Mu
- Data Loaders – William Eustis
- Graph Neural Networks (GNNS) – Minyang Tian & Michael Volk
- Inference Optimization with NVIDIA TensorRT – Nikil Ravi & Pranshu Chaturvedi
2021
- Getting Started with HAL – Dawei Mu
- Hands-on Deep Learning for Computer Vision – Asad Khan
- Intro to Tensorflow – Asad Khan
- Intro to PyTorch – Yao-Yu Lin
- Data Loaders wtih PyTorch – WIlliam Eustis
- Machine Learning with H20 on HAL – Dawei Mu
- Distributed Deep Learning on HAL – Dawei Mu
- Introduction to Natural Language Processing (NLP) with PyTorch – Volodymyr Kindratenko
- OpenVINO™ Toolkit Integration with Tensorflow with Hands-on Practice Intel DevCloud – Kumar Vishwesh & Yamini Mimmagadda
- Robust Physics Informed Neural Networks – Avik Roy
- Physics Informed Deep Learning – Shawn Rosofsky
2020
- Getting started with HAL — Dawei Mu
- Machine learning with H2O on HAL — Dawei Mu
- Intro to deep learning on HAL — Asad Khan
- Intro to sequence models (RNN, LSTM) on HAL — Aaron Saxton
- Distributed deep learning on HAL with TensorFlow and PyTorch — Benjamin Rabe, Ke Xu, Aaron Saxton
- Deep reinforcement learning — William Wei
- Hyper parameter optimization — Aaron Saxton, Benjamin Rabe
- AI for computer vision — Ke Xu