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 with the main prize being Titan V GPU cards.
Upcoming hackathon events
Past hackathon events
- 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
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 like the HAL cluster at NCSA.
If learning about AI appeals to you, attend one of our training sessions.
Upcoming training events
- HAL Fall 2021 training — September–November
Archived training materials
- 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
- 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