📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of RLHF (Reinforcement Learning from Human Feedback), from foundations to a certified capstone project.
Outline
Understand core RL concepts and Markov decision processes • Explore human feedback mechanisms and preference learning • Implement baseline RL agents in Python
Outline
Design crowdsourcing workflows for preference data • Apply quality‑control techniques and bias mitigation • Curate industrial datasets for RLHF experiments
Outline
Train reward models from human preferences • Validate reward signals with offline evaluation • Debug reward mis‑specification issues
Outline
Apply Proximal Policy Optimization (PPO) with reward models • Integrate KL‑regularization for safe fine‑tuning • Scale training on GPU clusters
Outline
Design automated and human‑in‑the‑loop evaluation metrics • Detect and mitigate harmful behaviors • Prepare audit reports for compliance
Outline
Define a real‑world RLHF use‑case • Build end‑to‑end pipeline from data collection to deployment • Present findings and receive mentor feedback
Earn government-registered certification in RLHF (Reinforcement Learning from Human Feedback)
e-Certificate and e-Marksheet issued on successful completion.
