IL - AI – 201 Reinforcement Learning in Process Controls - Instructor-Led
This course provides participants with the background knowledge and skills needed to create anomaly detectors, soft sensors, and process control models using advanced machine learning & reinforcement learning techniques. Through a combination of presented materials, Python code exercises, and data engineering & modeling challenges, participants will gain the ability to produce soft sensors and control models that can be loaded into the Ai-OPs Koios platform.
Description
Duration: 12 hours (instructor-led)
Dates & Times: By appointment
Location: At TriNova location
Instructor: Thomas Casey, Ph.D.
Advanced Level
AI – 201
Day1:
Section 1 – Advanced Python
Creating custom Python libraries
Documenting and sharing Python libraries
Section 2 – Advanced Data Engineering
Importing, cleaning, and characterizing process data
Advanced metrics & visualization techniques
Day 2:
Section 3 – Advanced Data Science
Review of statistical analyses techniques
Review of machine learning techniques
Section 4 – Process monitoring and control with ML & RL
Anomaly Detection in process data
Soft Sensors for process simulation
Creating process control models with Reinforcement Learning
Prerequisites
Prerequisites: Participants should be familiar with Python and statistical analysis techniques and have at least a basic understanding of machine learning techniques such as regression, classification, and reinforcement learning.