Foundations of Pattern & Anomaly Detection Systems

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As one of the seven patterns of AI, pattern and anomaly detection systems identify patterns in the data and learn higher order connections between information that can provide insight into whether a given piece of data fits an existing pattern or is an outlier and doesn’t fit. This course provides an overview of  pattern & anomaly detection, which algorithms are commonly used for this pattern, and use case examples. This course is geared towards a more technical audience. 

What Will I Learn?

  • Pattern and anomaly detection Overview
  • Use cases and applications of pattern and anomaly detection
  • Spotting outliers with AI
  • Traditional / Non-ML Approaches pattern and anomaly detection
  • Traditional / Non-ML Approaches to Predictive Analytics
  • Methods for pattern and anomaly detection on large data sets
  • Data visualization and pattern / anomaly detection
  • Data collection and preparation needs and issues for pattern / anomaly detection
  • Tools and technologies for pattern & anomaly detection

Who is this Training For?

  • Computer Systems Programmer
  • Developer
  • Artificial Intelligence Research Associate
  • Data Scientist
  • AI / Machine Learning (ML) Engineer
  • AI Assurance Engineer
  • Test & Evaluation Engineer, System Engineer
  • Network Analyst
  • Data Analyst
  • Operations Research Analyst
  • Deployment Engineer
  • Knowledge Operations Manager
  • Network Infrastructure Engineer
  • Information Technician

Supported Learning Paths

  • Cognilytica: Procuring AI 
  • DoD JAIC: Create AI
  • Edison DSF: Data Science Professionals (DSP04 -DSP09), Data Engineering & Management Professionals (DSP10-DSP16)

Learning Levels

  • DoD JAIC AI: Intermediate (Applied) Level
  • Edison DSF: Level 3-4
  • Category: Foundations of AI & Data Science