Applied Fundamentals of Industrial AI, Machine Learning and IIoT by Rajiv Anand, Quartic.AI, Xiaozhou Wang, Quartic.ai
Maintenance and Reliability engineers, managers and leaders; industrial process control, automation, MES and operational excellence professionals.
The workshop will serve as a primer for those who have little to no knowledge of data science, AI, and IIoT; and provide a concise, structured learning and reinforcement of topics for those with some basic understanding and knowledge.
PART I – FUNDAMENTALS
Data vs. insights – how do we extract insights from data
What is artificial intelligence; the type of problems it is solving.
What is machine learning and data science, how does it work and how is it different from traditional logic and rule based systems
Learning methods: How does an algorithm learn:
- Supervised Learning
- Unsupervised Learning
- Reinforcement learning
Different algorithm types and their application:
Patterns in data: pattern recognition, anomaly detection
- Gausian models
- Neural networks and deep learning
Machine Learning implementation process
Data types, basic exploration of data with statistical methods
Co-relation and causation
What are features, feature selection, extraction and importance
Model training, validation and optimization
Model performance evaluation and tuning
Quiz: Fundamentals reinforcement
PART II: IMPLEMENTATION
Deployment of Models:
Data processing basics – batch and streaming data processing
Cloud, Fog and edge
Data pipeline and IIoT architectures
Hands-on demonstration and exercise:
Using a modern industrial IIoT system, a step-by-step demonstration of building machine learning applications for anomaly detection and health prediction by an industrial user with no data science or programming experience.
Model Building Exercise:
Limited number of users will be able to build machine learning models for a prediction of faults and performance of a hydraulics system using a problem set and data set provided for the workshop