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
The AI for Waste Reduction and Resource Optimization course explores how artificial intelligence empowers sustainable decision‑making across industries. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The AI for Waste Reduction and Resource Optimization course explores how artificial intelligence empowers sustainable decision‑making across industries.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• 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 – QUANTIFY – AI‑Driven Waste Identification & Characterization
Leverage state‑of‑the‑art object detection (YOLO) to automate recycling and sorting. • Generate synthetic data and apply transfer learning to overcome scarce labelled datasets. • Integrate RGB, hyperspectral, and infrared imagery for material composition detection.
Day 2 – PREDICT – Demand Forecasting & Resource Conservation
Build regression and time‑series models to forecast resource consumption and avoid surplus. • Engineer features from weather, market trends, and IoT sensor data for robust pipelines. • Apply predictive‑maintenance models to reduce equipment scrap and energy waste.
Day 3 – OPTIMIZE – Operations Research & Reinforcement Learning for Circular Systems
Implement genetic algorithms and heuristics for smart logistics and route optimisation. • Design reinforcement‑learning agents for dynamic resource allocation. • Map AI outputs to Life Cycle Assessment (LCA) frameworks for quantifiable carbon‑reduction reporting.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | YOLO |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | Random Forest |
| Covered Tool / Platform | OR-Tools |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Scikit-learn |
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