Applications of AI for Anomaly Detection

8 hours of instruction

Learn to detect anomalies in large data sets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).

OBJECTIVES

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

PREREQUISITES

None

SYLLABUS & TOPICS COVERED

1. Introduction
   • Meet the instructor
   • Create an account
2. GPU Accelerated XG Boost
   • Prepare data for GPU acceleration using the provided dataset.
   • Train a binary and multi-class classifier using the popular machine learning
   • algorithm XGBoost.
   • Assess and improve your model’s performance before deployment.
3. GPU Accelerated Autoencoders
   • Build and train a deep learning-based autoencoder to work with unlabeled data.
   • Apply techniques to separate anomalies into multiple classes.
   • Explore other applications of GPU-accelerated autoencoders.
4. Project
   • Train an unsupervised learning model to create new data.
   • Use that new data to turn the problem into a supervised learning problem.
   • Compare the performance of this new approach to more established approaches.

SOFTWARE REQUIREMENTS

Each participant will be provided with dedicated access to a fully configured, GPU-accelerated workstation in the cloud.