Artificial Intelligence (AI) in Automotive

The participants must have programming experience (any language) and engineering background, but are not required to write any code during the course.

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.

Current state of the technology

• What is used
• What may be potentially used

Rules based AI 

• Simplifying decision

Machine Learning 

• Classification
• Clustering
• Neural Networks
• Types of Neural Networks
• Presentation of working examples and discussion

Deep Learning

• Basic vocabulary 
• When to use Deep Learning, when not to
• Estimating computational resources and cost
• Very short theoretical background to Deep Neural Networks

Deep Learning in practice (mainly using TensorFlow)

• Preparing Data
• Choosing loss function
• Choosing appropriate type on neural network
• Accuracy vs speed and resources
• Training neural network
• Measuring efficiency and error

Sample usage

• Anomaly detection
• Image recognition
• ADAS