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 Duration 14 hours

Course Outline

The current state of the technology

  • Existing applications
  • Potential future uses

Rule-based AI

  • Simplifying decision processes

Machine Learning

  • Classification
  • Clustering
  • Neural Networks
  • Various types of Neural Networks
  • Demonstration of working examples and discussion

Deep Learning

  • Foundational terminology
  • Criteria for selecting or avoiding Deep Learning
  • Assessing computational requirements and costs
  • Brief theoretical overview of Deep Neural Networks

Deep Learning in practice (primarily using TensorFlow)

  • Data preparation
  • Selecting loss functions
  • Choosing the right neural network architecture
  • Optimizing accuracy against speed and resource usage
  • Neural network training
  • Evaluating efficiency and error metrics

Use cases

  • Anomaly detection
  • Image recognition
  • ADAS

Requirements

Participants are expected to have a background in engineering and programming experience in any language. However, no coding skills are required for practical tasks during the course.

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