Programme Parameters
Graduate / Intermediate
3 Weeks (30 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
That focuses on the crucial role of human feedback in enhancing AI performance, safety, and ethical behavior. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Human-in-the-Loop: AI Training and RLHF is a cutting-edge course that focuses on the crucial role of human feedback in enhancing AI performance, safety, and ethical behavior.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 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
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• 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
Understanding Human-in-the-Loop (HITL) Systems
Define core principles of Human-in-the-Loop Learning and its role in modern AI pipelines • Analyze the role of humans in model training, testing, and continuous monitoring workflows • Compare feedback modalities including labels, rankings, preferences, and corrections
Introduction to RLHF (Reinforcement Learning from Human Feedback)
Evaluate why traditional supervised learning falls short for complex AI alignment tasks • Identify core components of RLHF pipelines and their interdependencies • Examine real-world examples including GPT alignment, code assistants, and human evaluation
Collecting and Using Human Feedback
Design effective annotation interfaces and comprehensive task guidelines for labelers • Implement labeler training, calibration protocols, and bias reduction strategies • Apply ranking, preference comparison, and paired evaluation techniques for quality feedback
Reward Modeling and Fine-Tuning
Build robust reward models from aggregated human feedback signals • Execute fine-tuning with PPO (Proximal Policy Optimization) for policy improvement • Align LLMs with RLHF objectives while balancing human control and model capability
Operationalizing HITL at Scale
Deploy Human-in-the-Loop workflows in production AI environments • Leverage active learning and iterative retraining for continuous model improvement • Integrate APIs, dashboards, and automated feedback loops for scalable operations
Governance, Safety, and the Future of Human Feedback
Assess limitations and risks inherent in RLHF implementations • Navigate ethical and legal considerations in HITL system design • Balance human-AI collaboration with appropriate control mechanisms
Capstone Project – Building an RLHF-Aligned System
Architect end-to-end RLHF pipelines from feedback collection to model deployment • Validate system performance against safety, helpfulness, and harmlessness criteria • Present solutions to expert panel for feedback and industry readiness assessment
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Hugging Face Transformers |
| Covered Tool / Platform | TRL |
| Covered Tool / Platform | OpenAI Gym |
| Covered Tool / Platform | PPO |
| Covered Tool / Platform | Label Studio |
| Covered Tool / Platform | Prodigy |
| Covered Tool / Platform | Anthropic HH-RLHF |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
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