Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Autonomous Industrial Systems and Process Optimization explores how machine learning is pushing industry toward self-running, self-improving operations. You learn how AI enables autonomous monitoring and control of industrial processes, closed-loop optimisation that continuously tunes for efficiency and quality, and the reinforcement-learning and control methods behind them. The course covers the sensing and data foundations required, and the real-world constraints of trusting automation with production. You finish able to reason about designing an AI-driven autonomous process-optimisation system. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI for autonomous industrial systems — self-optimising processes, autonomous control and closed-loop optimisation across industrial operations.
📋 Course Objectives
1. Explain autonomous monitoring and control.
2. Apply closed-loop optimisation to processes.
3. Use RL and control methods for industry.
4. Build on sensing and data foundations.
5. Address trust and safety in industrial automation.
👥 Who Should Enroll?
• Process and control engineers
• Industrial automation professionals
• Data scientists in manufacturing
• Students of industrial AI
🚀 Key Learning Outcomes
• An understanding of autonomous industrial AI.
• A process-optimisation perspective.
• An applied industrial-automation project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Industrial Data & Problem Understanding
Identify autonomous industrial systems and their data sources • Load and clean real‑world sensor, log, and process datasets • Explore feature characteristics for downstream modeling
Day 2 – AI for Optimization & Fault Detection
Build classification models for fault detection • Develop regression models to predict process efficiency • Validate model performance on industrial benchmarks
Day 3 – Optimization, Insights & Decision Systems
Interpret model outputs and extract feature importance • Apply basic optimization logic to generate actionable suggestions • Create a simple decision‑support dashboard for scenario testing
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Excel |
Frequently Asked Questions
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