Data integrity is critical to research and business. AI models can be trained and deployed to automatically analyze datasets, define “normal behavior,” and identify breaches in patterns quickly and effectively. These models can then be used to predict future anomalies. With massive amounts of data available online and across research domains and subtle distinctions between normal and abnormal patterns, it’s critical to use AI to quickly detect anomalies that pose a threat.

In this workshop, you’ll learn how to implement multiple AI-based approaches to solve a specific use case of identifying network intrusions for telecommunications. You’ll learn three different anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques. At the end of the workshop, you’ll be able to use AI to detect anomalies in your research in different domains.

This 8-hour workshop is split into two 4-hour sessions.

Learning Objectives:

By participating in this workshop, you’ll

• Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GAN
• Detect anomalies in datasets with both labeled and unlabeled data
• Classify anomalies into multiple categories regardless of whether the original data was labeled

Prerequisites

• Basic Python competency
• Basic understanding of deep neural networks
• No previous knowledge of CUDA programming is required.

Suggested materials to satisfy prerequisites:

• The Python Tutorial
• Getting started with Deep Learning

Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Workshop provided by Nvidia and Research Computing. Any questions, please contact Ying Zhang at yingz@ufl.edu.