Anomaly Detection - a Machine Learning Approach
This session addresses anomaly detection from a data science / machine learning point of view. We will review some machine learning basics and then put these ideas to work in a live/demo scenario. Using Jupyter Notebooks within Amazon Web Services SageMaker platform, our speaker will demonstrate how data sets can be used to train algorithms to find anomalous events. There will be time for questions and discussion is encouraged in order to share intuitions around the use of this approach to address cyber-security concerns.
Michael Golub is a data science evangelist and researcher with a focus on the practical application and democratization of AI. With over 25 years of application development and analytics experience he brings a pragmatic approach to machine learning solutions while sharing his enthusiasm for this game changing technology. Michael is also a member of Drexel’s Bachelors Degree in Data Science Industry Board.